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< a href = "sparse_8h.html" > Go to the documentation of this file.< / a > < div class = "fragment" > < div class = "line" > < a id = "l00001" name = "l00001" > < / a > < span class = "lineno" > 1< / span > < span class = "comment" > // Copyright 2010-2021 Google LLC< / span > < / div >
< div class = "line" > < a id = "l00002" name = "l00002" > < / a > < span class = "lineno" > 2< / span > < span class = "comment" > // Licensed under the Apache License, Version 2.0 (the " License" );< / span > < / div >
< div class = "line" > < a id = "l00003" name = "l00003" > < / a > < span class = "lineno" > 3< / span > < span class = "comment" > // you may not use this file except in compliance with the License.< / span > < / div >
< div class = "line" > < a id = "l00004" name = "l00004" > < / a > < span class = "lineno" > 4< / span > < span class = "comment" > // You may obtain a copy of the License at< / span > < / div >
< div class = "line" > < a id = "l00005" name = "l00005" > < / a > < span class = "lineno" > 5< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00006" name = "l00006" > < / a > < span class = "lineno" > 6< / span > < span class = "comment" > // http://www.apache.org/licenses/LICENSE-2.0< / span > < / div >
< div class = "line" > < a id = "l00007" name = "l00007" > < / a > < span class = "lineno" > 7< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00008" name = "l00008" > < / a > < span class = "lineno" > 8< / span > < span class = "comment" > // Unless required by applicable law or agreed to in writing, software< / span > < / div >
< div class = "line" > < a id = "l00009" name = "l00009" > < / a > < span class = "lineno" > 9< / span > < span class = "comment" > // distributed under the License is distributed on an " AS IS" BASIS,< / span > < / div >
< div class = "line" > < a id = "l00010" name = "l00010" > < / a > < span class = "lineno" > 10< / span > < span class = "comment" > // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.< / span > < / div >
< div class = "line" > < a id = "l00011" name = "l00011" > < / a > < span class = "lineno" > 11< / span > < span class = "comment" > // See the License for the specific language governing permissions and< / span > < / div >
< div class = "line" > < a id = "l00012" name = "l00012" > < / a > < span class = "lineno" > 12< / span > < span class = "comment" > // limitations under the License.< / span > < / div >
< div class = "line" > < a id = "l00013" name = "l00013" > < / a > < span class = "lineno" > 13< / span > < / div >
< div class = "line" > < a id = "l00014" name = "l00014" > < / a > < span class = "lineno" > 14< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00015" name = "l00015" > < / a > < span class = "lineno" > 15< / span > < span class = "comment" > // The following are very good references for terminology, data structures,< / span > < / div >
< div class = "line" > < a id = "l00016" name = "l00016" > < / a > < span class = "lineno" > 16< / span > < span class = "comment" > // and algorithms:< / span > < / div >
< div class = "line" > < a id = "l00017" name = "l00017" > < / a > < span class = "lineno" > 17< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00018" name = "l00018" > < / a > < span class = "lineno" > 18< / span > < span class = "comment" > // I.S. Duff, A.M. Erisman and J.K. Reid, " Direct Methods for Sparse Matrices" ,< / span > < / div >
< div class = "line" > < a id = "l00019" name = "l00019" > < / a > < span class = "lineno" > 19< / span > < span class = "comment" > // Clarendon, Oxford, UK, 1987, ISBN 0-19-853421-3,< / span > < / div >
< div class = "line" > < a id = "l00020" name = "l00020" > < / a > < span class = "lineno" > 20< / span > < span class = "comment" > // http://www.amazon.com/dp/0198534213.< / span > < / div >
< div class = "line" > < a id = "l00021" name = "l00021" > < / a > < span class = "lineno" > 21< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00022" name = "l00022" > < / a > < span class = "lineno" > 22< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00023" name = "l00023" > < / a > < span class = "lineno" > 23< / span > < span class = "comment" > // T.A. Davis, " Direct methods for Sparse Linear Systems" , SIAM, Philadelphia,< / span > < / div >
< div class = "line" > < a id = "l00024" name = "l00024" > < / a > < span class = "lineno" > 24< / span > < span class = "comment" > // 2006, ISBN-13: 978-0-898716-13, http://www.amazon.com/dp/0898716136.< / span > < / div >
< div class = "line" > < a id = "l00025" name = "l00025" > < / a > < span class = "lineno" > 25< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00026" name = "l00026" > < / a > < span class = "lineno" > 26< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00027" name = "l00027" > < / a > < span class = "lineno" > 27< / span > < span class = "comment" > // Both books also contain a wealth of references.< / span > < / div >
< div class = "line" > < a id = "l00028" name = "l00028" > < / a > < span class = "lineno" > 28< / span > < / div >
< div class = "line" > < a id = "l00029" name = "l00029" > < / a > < span class = "lineno" > 29< / span > < span class = "preprocessor" > #ifndef OR_TOOLS_LP_DATA_SPARSE_H_< / span > < / div >
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< div class = "line" > < a id = "l00034" name = "l00034" > < / a > < span class = "lineno" > 34< / span > < / div >
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< div class = "line" > < a id = "l00036" name = "l00036" > < / a > < span class = "lineno" > 36< / span > < span class = "preprocessor" > #include " < a class = "code" href = "lp__types_8h.html" > ortools/lp_data/lp_types.h< / a > " < / span > < / div >
< div class = "line" > < a id = "l00037" name = "l00037" > < / a > < span class = "lineno" > 37< / span > < span class = "preprocessor" > #include " < a class = "code" href = "lp__data_2permutation_8h.html" > ortools/lp_data/permutation.h< / a > " < / span > < / div >
< div class = "line" > < a id = "l00038" name = "l00038" > < / a > < span class = "lineno" > 38< / span > < span class = "preprocessor" > #include " < a class = "code" href = "scattered__vector_8h.html" > ortools/lp_data/scattered_vector.h< / a > " < / span > < / div >
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< div class = "line" > < a id = "l00040" name = "l00040" > < / a > < span class = "lineno" > 40< / span > < span class = "preprocessor" > #include " < a class = "code" href = "return__macros_8h.html" > ortools/util/return_macros.h< / a > " < / span > < / div >
< div class = "line" > < a id = "l00041" name = "l00041" > < / a > < span class = "lineno" > 41< / span > < / div >
< div class = "line" > < a id = "l00042" name = "l00042" > < / a > < span class = "lineno" > 42< / span > < span class = "keyword" > namespace < / span > < a class = "code hl_namespace" href = "namespaceoperations__research.html" > operations_research< / a > {< / div >
< div class = "line" > < a id = "l00043" name = "l00043" > < / a > < span class = "lineno" > 43< / span > < span class = "keyword" > namespace < / span > glop {< / div >
< div class = "line" > < a id = "l00044" name = "l00044" > < / a > < span class = "lineno" > 44< / span > < / div >
< div class = "line" > < a id = "l00045" name = "l00045" > < / a > < span class = "lineno" > 45< / span > < span class = "keyword" > class < / span > CompactSparseMatrixView;< / div >
< div class = "line" > < a id = "l00046" name = "l00046" > < / a > < span class = "lineno" > 46< / span > < / div >
< div class = "line" > < a id = "l00047" name = "l00047" > < / a > < span class = "lineno" > 47< / span > < span class = "comment" > // --------------------------------------------------------< / span > < / div >
< div class = "line" > < a id = "l00048" name = "l00048" > < / a > < span class = "lineno" > 48< / span > < span class = "comment" > // SparseMatrix< / span > < / div >
< div class = "line" > < a id = "l00049" name = "l00049" > < / a > < span class = "lineno" > 49< / span > < span class = "comment" > // --------------------------------------------------------< / span > < / div >
< div class = "line" > < a id = "l00050" name = "l00050" > < / a > < span class = "lineno" > 50< / span > < span class = "comment" > // SparseMatrix is a class for sparse matrices suitable for computation.< / span > < / div >
< div class = "line" > < a id = "l00051" name = "l00051" > < / a > < span class = "lineno" > 51< / span > < span class = "comment" > // Data is represented using the so-called compressed-column storage scheme.< / span > < / div >
< div class = "line" > < a id = "l00052" name = "l00052" > < / a > < span class = "lineno" > 52< / span > < span class = "comment" > // Entries (row, col, value) are stored by column using a SparseColumn.< / span > < / div >
< div class = "line" > < a id = "l00053" name = "l00053" > < / a > < span class = "lineno" > 53< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00054" name = "l00054" > < / a > < span class = "lineno" > 54< / span > < span class = "comment" > // Citing [Duff et al, 1987], a matrix is sparse if many of its coefficients are< / span > < / div >
< div class = "line" > < a id = "l00055" name = "l00055" > < / a > < span class = "lineno" > 55< / span > < span class = "comment" > // zero and if there is an advantage in exploiting its zeros.< / span > < / div >
< div class = "line" > < a id = "l00056" name = "l00056" > < / a > < span class = "lineno" > 56< / span > < span class = "comment" > // For practical reasons, not all zeros are exploited (for example those that< / span > < / div >
< div class = "line" > < a id = "l00057" name = "l00057" > < / a > < span class = "lineno" > 57< / span > < span class = "comment" > // result from calculations.) The term entry refers to those coefficients that< / span > < / div >
< div class = "line" > < a id = "l00058" name = "l00058" > < / a > < span class = "lineno" > 58< / span > < span class = "comment" > // are handled explicitly. All non-zeros are entries while some zero< / span > < / div >
< div class = "line" > < a id = "l00059" name = "l00059" > < / a > < span class = "lineno" > 59< / span > < span class = "comment" > // coefficients may also be entries.< / span > < / div >
< div class = "line" > < a id = "l00060" name = "l00060" > < / a > < span class = "lineno" > 60< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00061" name = "l00061" > < / a > < span class = "lineno" > 61< / span > < span class = "comment" > // Note that no special ordering of entries is assumed.< / span > < / div >
< div class = "line" > < a id = "l00062" name = "l00062" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html" > 62< / a > < / span > < span class = "keyword" > class < / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html" > SparseMatrix< / a > {< / div >
< div class = "line" > < a id = "l00063" name = "l00063" > < / a > < span class = "lineno" > 63< / span > < span class = "keyword" > public< / span > :< / div >
< div class = "line" > < a id = "l00064" name = "l00064" > < / a > < span class = "lineno" > 64< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a79446a803c1bed8b17c8ac937d07be39" > SparseMatrix< / a > ();< / div >
< div class = "line" > < a id = "l00065" name = "l00065" > < / a > < span class = "lineno" > 65< / span > < / div >
< div class = "line" > < a id = "l00066" name = "l00066" > < / a > < span class = "lineno" > 66< / span > < span class = "comment" > // Useful for testing. This makes it possible to write:< / span > < / div >
< div class = "line" > < a id = "l00067" name = "l00067" > < / a > < span class = "lineno" > 67< / span > < span class = "comment" > // SparseMatrix matrix {< / span > < / div >
< div class = "line" > < a id = "l00068" name = "l00068" > < / a > < span class = "lineno" > 68< / span > < span class = "comment" > // {1, 2, 3},< / span > < / div >
< div class = "line" > < a id = "l00069" name = "l00069" > < / a > < span class = "lineno" > 69< / span > < span class = "comment" > // {4, 5, 6},< / span > < / div >
< div class = "line" > < a id = "l00070" name = "l00070" > < / a > < span class = "lineno" > 70< / span > < span class = "comment" > // {7, 8, 9}};< / span > < / div >
< div class = "line" > < a id = "l00071" name = "l00071" > < / a > < span class = "lineno" > 71< / span > < span class = "preprocessor" > #if (!defined(_MSC_VER) || _MSC_VER > = 1800)< / span > < / div >
< div class = "line" > < a id = "l00072" name = "l00072" > < / a > < span class = "lineno" > 72< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a79446a803c1bed8b17c8ac937d07be39" > SparseMatrix< / a > (< / div >
< div class = "line" > < a id = "l00073" name = "l00073" > < / a > < span class = "lineno" > 73< / span > std::initializer_list< std::initializer_list< Fractional> > init_list);< / div >
< div class = "line" > < a id = "l00074" name = "l00074" > < / a > < span class = "lineno" > 74< / span > < span class = "preprocessor" > #endif< / span > < / div >
< div class = "line" > < a id = "l00075" name = "l00075" > < / a > < span class = "lineno" > 75< / span > < span class = "comment" > // Clears internal data structure, i.e. erases all the columns and set< / span > < / div >
< div class = "line" > < a id = "l00076" name = "l00076" > < / a > < span class = "lineno" > 76< / span > < span class = "comment" > // the number of rows to zero.< / span > < / div >
< div class = "line" > < a id = "l00077" name = "l00077" > < / a > < span class = "lineno" > 77< / span > < span class = "keywordtype" > void< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#aa71d36872f416feaa853788a7a7a7ef8" > Clear< / a > ();< / div >
< div class = "line" > < a id = "l00078" name = "l00078" > < / a > < span class = "lineno" > 78< / span > < / div >
< div class = "line" > < a id = "l00079" name = "l00079" > < / a > < span class = "lineno" > 79< / span > < span class = "comment" > // Returns true if the matrix is empty.< / span > < / div >
< div class = "line" > < a id = "l00080" name = "l00080" > < / a > < span class = "lineno" > 80< / span > < span class = "comment" > // That is if num_rows() OR num_cols() are zero.< / span > < / div >
< div class = "line" > < a id = "l00081" name = "l00081" > < / a > < span class = "lineno" > 81< / span > < span class = "keywordtype" > bool< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a8e12342fc420701fbffd97025421575a" > IsEmpty< / a > () < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00082" name = "l00082" > < / a > < span class = "lineno" > 82< / span > < / div >
< div class = "line" > < a id = "l00083" name = "l00083" > < / a > < span class = "lineno" > 83< / span > < span class = "comment" > // Cleans the columns, i.e. removes zero-values entries, removes duplicates< / span > < / div >
< div class = "line" > < a id = "l00084" name = "l00084" > < / a > < span class = "lineno" > 84< / span > < span class = "comment" > // entries and sorts remaining entries in increasing row order.< / span > < / div >
< div class = "line" > < a id = "l00085" name = "l00085" > < / a > < span class = "lineno" > 85< / span > < span class = "comment" > // Call with care: Runs in O(num_cols * column_cleanup), with each column< / span > < / div >
< div class = "line" > < a id = "l00086" name = "l00086" > < / a > < span class = "lineno" > 86< / span > < span class = "comment" > // cleanup running in O(num_entries * log(num_entries)).< / span > < / div >
< div class = "line" > < a id = "l00087" name = "l00087" > < / a > < span class = "lineno" > 87< / span > < span class = "keywordtype" > void< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#abfc30f91ab75c6f4552003f777672e74" > CleanUp< / a > ();< / div >
< div class = "line" > < a id = "l00088" name = "l00088" > < / a > < span class = "lineno" > 88< / span > < / div >
< div class = "line" > < a id = "l00089" name = "l00089" > < / a > < span class = "lineno" > 89< / span > < span class = "comment" > // Call CheckNoDuplicates() on all columns, useful for doing a DCHECK.< / span > < / div >
< div class = "line" > < a id = "l00090" name = "l00090" > < / a > < span class = "lineno" > 90< / span > < span class = "keywordtype" > bool< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a2e5611d47d02e1029b98a8e9bee3469f" > CheckNoDuplicates< / a > () < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00091" name = "l00091" > < / a > < span class = "lineno" > 91< / span > < / div >
< div class = "line" > < a id = "l00092" name = "l00092" > < / a > < span class = "lineno" > 92< / span > < span class = "comment" > // Call IsCleanedUp() on all columns, useful for doing a DCHECK.< / span > < / div >
< div class = "line" > < a id = "l00093" name = "l00093" > < / a > < span class = "lineno" > 93< / span > < span class = "keywordtype" > bool< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a5e016d204d43b2cc4a2773c25462968a" > IsCleanedUp< / a > () < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00094" name = "l00094" > < / a > < span class = "lineno" > 94< / span > < / div >
< div class = "line" > < a id = "l00095" name = "l00095" > < / a > < span class = "lineno" > 95< / span > < span class = "comment" > // Change the number of row of this matrix.< / span > < / div >
< div class = "line" > < a id = "l00096" name = "l00096" > < / a > < span class = "lineno" > 96< / span > < span class = "keywordtype" > void< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a55265e26d9e69e2d7a882bab054b8139" > SetNumRows< / a > (RowIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a960110e64357a3e69162ebf1f71959dd" > num_rows< / a > );< / div >
< div class = "line" > < a id = "l00097" name = "l00097" > < / a > < span class = "lineno" > 97< / span > < / div >
< div class = "line" > < a id = "l00098" name = "l00098" > < / a > < span class = "lineno" > 98< / span > < span class = "comment" > // Appends an empty column and returns its index.< / span > < / div >
< div class = "line" > < a id = "l00099" name = "l00099" > < / a > < span class = "lineno" > 99< / span > ColIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a86fd105be79f4f2dbaf3a21e64e4d022" > AppendEmptyColumn< / a > ();< / div >
< div class = "line" > < a id = "l00100" name = "l00100" > < / a > < span class = "lineno" > 100< / span > < / div >
< div class = "line" > < a id = "l00101" name = "l00101" > < / a > < span class = "lineno" > 101< / span > < span class = "comment" > // Appends a unit vector defined by the single entry (row, value).< / span > < / div >
< div class = "line" > < a id = "l00102" name = "l00102" > < / a > < span class = "lineno" > 102< / span > < span class = "comment" > // Note that the row should be smaller than the number of rows of the matrix.< / span > < / div >
< div class = "line" > < a id = "l00103" name = "l00103" > < / a > < span class = "lineno" > 103< / span > < span class = "keywordtype" > void< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a06d22c92f8b45b18560b46797f98a81b" > AppendUnitVector< / a > (RowIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aea35f36ba98d5bbd8d033382f50c9e52" > row< / a > , < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > < a class = "code hl_variable" href = "demon__profiler_8cc.html#ac072af30c4ffbc834bb4c681f6ecb514" > value< / a > );< / div >
< div class = "line" > < a id = "l00104" name = "l00104" > < / a > < span class = "lineno" > 104< / span > < / div >
< div class = "line" > < a id = "l00105" name = "l00105" > < / a > < span class = "lineno" > 105< / span > < span class = "comment" > // Swaps the content of this SparseMatrix with the one passed as argument.< / span > < / div >
< div class = "line" > < a id = "l00106" name = "l00106" > < / a > < span class = "lineno" > 106< / span > < span class = "comment" > // Works in O(1).< / span > < / div >
< div class = "line" > < a id = "l00107" name = "l00107" > < / a > < span class = "lineno" > 107< / span > < span class = "keywordtype" > void< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a6052657cbffbe19decf328bf369d58e1" > Swap< / a > (< a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html" > SparseMatrix< / a > * matrix);< / div >
< div class = "line" > < a id = "l00108" name = "l00108" > < / a > < span class = "lineno" > 108< / span > < / div >
< div class = "line" > < a id = "l00109" name = "l00109" > < / a > < span class = "lineno" > 109< / span > < span class = "comment" > // Populates the matrix with num_cols columns of zeros. As the number of rows< / span > < / div >
< div class = "line" > < a id = "l00110" name = "l00110" > < / a > < span class = "lineno" > 110< / span > < span class = "comment" > // is specified by num_rows, the matrix is not necessarily square.< / span > < / div >
< div class = "line" > < a id = "l00111" name = "l00111" > < / a > < span class = "lineno" > 111< / span > < span class = "comment" > // Previous columns/values are deleted.< / span > < / div >
< div class = "line" > < a id = "l00112" name = "l00112" > < / a > < span class = "lineno" > 112< / span > < span class = "keywordtype" > void< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#ae1b90982a83e1b025ebbc1c446980640" > PopulateFromZero< / a > (RowIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a960110e64357a3e69162ebf1f71959dd" > num_rows< / a > , ColIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a41741829541d089f1c4d34f190884813" > num_cols< / a > );< / div >
< div class = "line" > < a id = "l00113" name = "l00113" > < / a > < span class = "lineno" > 113< / span > < / div >
< div class = "line" > < a id = "l00114" name = "l00114" > < / a > < span class = "lineno" > 114< / span > < span class = "comment" > // Populates the matrix from the Identity matrix of size num_cols.< / span > < / div >
< div class = "line" > < a id = "l00115" name = "l00115" > < / a > < span class = "lineno" > 115< / span > < span class = "comment" > // Previous columns/values are deleted.< / span > < / div >
< div class = "line" > < a id = "l00116" name = "l00116" > < / a > < span class = "lineno" > 116< / span > < span class = "keywordtype" > void< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a323801972c3d6de340e260de4582c34b" > PopulateFromIdentity< / a > (ColIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a41741829541d089f1c4d34f190884813" > num_cols< / a > );< / div >
< div class = "line" > < a id = "l00117" name = "l00117" > < / a > < span class = "lineno" > 117< / span > < / div >
< div class = "line" > < a id = "l00118" name = "l00118" > < / a > < span class = "lineno" > 118< / span > < span class = "comment" > // Populates the matrix from the transposed of the given matrix.< / span > < / div >
< div class = "line" > < a id = "l00119" name = "l00119" > < / a > < span class = "lineno" > 119< / span > < span class = "comment" > // Note that this preserve the property of lower/upper triangular matrix< / span > < / div >
< div class = "line" > < a id = "l00120" name = "l00120" > < / a > < span class = "lineno" > 120< / span > < span class = "comment" > // to have the diagonal coefficients first/last in each columns. It actually< / span > < / div >
< div class = "line" > < a id = "l00121" name = "l00121" > < / a > < span class = "lineno" > 121< / span > < span class = "comment" > // sorts the entries in each columns by their indices.< / span > < / div >
< div class = "line" > < a id = "l00122" name = "l00122" > < / a > < span class = "lineno" > 122< / span > < span class = "keyword" > template< / span > < < span class = "keyword" > typename< / span > Matrix> < / div >
< div class = "line" > < a id = "l00123" name = "l00123" > < / a > < span class = "lineno" > 123< / span > < span class = "keywordtype" > void< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a2fe6f7470512f5301031480737375c88" > PopulateFromTranspose< / a > (< span class = "keyword" > const< / span > Matrix& < a class = "code hl_function" href = "parser_8yy_8cc.html#a5a634cf4429798b1c921a81de8250051" > input< / a > );< / div >
< div class = "line" > < a id = "l00124" name = "l00124" > < / a > < span class = "lineno" > 124< / span > < / div >
< div class = "line" > < a id = "l00125" name = "l00125" > < / a > < span class = "lineno" > 125< / span > < span class = "comment" > // Populates a SparseMatrix from another one (copy), note that this run in< / span > < / div >
< div class = "line" > < a id = "l00126" name = "l00126" > < / a > < span class = "lineno" > 126< / span > < span class = "comment" > // O(number of entries in the matrix).< / span > < / div >
< div class = "line" > < a id = "l00127" name = "l00127" > < / a > < span class = "lineno" > 127< / span > < span class = "keywordtype" > void< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#acbbc88405e6db3fe77064e1a3d4e402a" > PopulateFromSparseMatrix< / a > (< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html" > SparseMatrix< / a > & matrix);< / div >
< div class = "line" > < a id = "l00128" name = "l00128" > < / a > < span class = "lineno" > 128< / span > < / div >
< div class = "line" > < a id = "l00129" name = "l00129" > < / a > < span class = "lineno" > 129< / span > < span class = "comment" > // Populates a SparseMatrix from the image of a matrix A through the given< / span > < / div >
< div class = "line" > < a id = "l00130" name = "l00130" > < / a > < span class = "lineno" > 130< / span > < span class = "comment" > // row_perm and inverse_col_perm. See permutation.h for more details.< / span > < / div >
< div class = "line" > < a id = "l00131" name = "l00131" > < / a > < span class = "lineno" > 131< / span > < span class = "keyword" > template< / span > < < span class = "keyword" > typename< / span > Matrix> < / div >
< div class = "line" > < a id = "l00132" name = "l00132" > < / a > < span class = "lineno" > 132< / span > < span class = "keywordtype" > void< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a29edb960f882c54b9652853e94988e79" > PopulateFromPermutedMatrix< / a > (< span class = "keyword" > const< / span > Matrix& < a class = "code hl_variable" href = "constraint__solver_2table_8cc.html#acb18315d548212835cd8ed4287e6c0b6" > a< / a > ,< / div >
< div class = "line" > < a id = "l00133" name = "l00133" > < / a > < span class = "lineno" > 133< / span > < span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_permutation.html" > RowPermutation< / a > & row_perm,< / div >
< div class = "line" > < a id = "l00134" name = "l00134" > < / a > < span class = "lineno" > 134< / span > < span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_permutation.html" > ColumnPermutation< / a > & inverse_col_perm);< / div >
< div class = "line" > < a id = "l00135" name = "l00135" > < / a > < span class = "lineno" > 135< / span > < / div >
< div class = "line" > < a id = "l00136" name = "l00136" > < / a > < span class = "lineno" > 136< / span > < span class = "comment" > // Populates a SparseMatrix from the result of alpha * A + beta * B,< / span > < / div >
< div class = "line" > < a id = "l00137" name = "l00137" > < / a > < span class = "lineno" > 137< / span > < span class = "comment" > // where alpha and beta are Fractionals, A and B are sparse matrices.< / span > < / div >
< div class = "line" > < a id = "l00138" name = "l00138" > < / a > < span class = "lineno" > 138< / span > < span class = "keywordtype" > void< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a0c406d94c3586159071e0b370a5a02fb" > PopulateFromLinearCombination< / a > (< a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > alpha, < span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html" > SparseMatrix< / a > & < a class = "code hl_variable" href = "constraint__solver_2table_8cc.html#acb18315d548212835cd8ed4287e6c0b6" > a< / a > ,< / div >
< div class = "line" > < a id = "l00139" name = "l00139" > < / a > < span class = "lineno" > 139< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > beta, < span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html" > SparseMatrix< / a > & < a class = "code hl_variable" href = "constraint__solver_2table_8cc.html#a9293e4d29cac928301645070dd307683" > b< / a > );< / div >
< div class = "line" > < a id = "l00140" name = "l00140" > < / a > < span class = "lineno" > 140< / span > < / div >
< div class = "line" > < a id = "l00141" name = "l00141" > < / a > < span class = "lineno" > 141< / span > < span class = "comment" > // Multiplies SparseMatrix a by SparseMatrix b.< / span > < / div >
< div class = "line" > < a id = "l00142" name = "l00142" > < / a > < span class = "lineno" > 142< / span > < span class = "keywordtype" > void< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#aa1c2872fa7d491d4c093c8b2124a53b9" > PopulateFromProduct< / a > (< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html" > SparseMatrix< / a > & < a class = "code hl_variable" href = "constraint__solver_2table_8cc.html#acb18315d548212835cd8ed4287e6c0b6" > a< / a > , < span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html" > SparseMatrix< / a > & < a class = "code hl_variable" href = "constraint__solver_2table_8cc.html#a9293e4d29cac928301645070dd307683" > b< / a > );< / div >
< div class = "line" > < a id = "l00143" name = "l00143" > < / a > < span class = "lineno" > 143< / span > < / div >
< div class = "line" > < a id = "l00144" name = "l00144" > < / a > < span class = "lineno" > 144< / span > < span class = "comment" > // Removes the marked columns from the matrix and adjust its size.< / span > < / div >
< div class = "line" > < a id = "l00145" name = "l00145" > < / a > < span class = "lineno" > 145< / span > < span class = "comment" > // This runs in O(num_cols).< / span > < / div >
< div class = "line" > < a id = "l00146" name = "l00146" > < / a > < span class = "lineno" > 146< / span > < span class = "keywordtype" > void< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#ac59fd9cddfe284bf9dc7581ed631ce8d" > DeleteColumns< / a > (< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseBooleanRow< / a > & columns_to_delete);< / div >
< div class = "line" > < a id = "l00147" name = "l00147" > < / a > < span class = "lineno" > 147< / span > < / div >
< div class = "line" > < a id = "l00148" name = "l00148" > < / a > < span class = "lineno" > 148< / span > < span class = "comment" > // Applies the given row permutation and deletes the rows for which< / span > < / div >
< div class = "line" > < a id = "l00149" name = "l00149" > < / a > < span class = "lineno" > 149< / span > < span class = "comment" > // permutation[row] is kInvalidRow. Sets the new number of rows to num_rows.< / span > < / div >
< div class = "line" > < a id = "l00150" name = "l00150" > < / a > < span class = "lineno" > 150< / span > < span class = "comment" > // This runs in O(num_entries).< / span > < / div >
< div class = "line" > < a id = "l00151" name = "l00151" > < / a > < span class = "lineno" > 151< / span > < span class = "keywordtype" > void< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a860e7a37e8c7c2f5f7f0a73d3e3473f0" > DeleteRows< / a > (RowIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a960110e64357a3e69162ebf1f71959dd" > num_rows< / a > , < span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_permutation.html" > RowPermutation< / a > & permutation);< / div >
< div class = "line" > < a id = "l00152" name = "l00152" > < / a > < span class = "lineno" > 152< / span > < / div >
< div class = "line" > < a id = "l00153" name = "l00153" > < / a > < span class = "lineno" > 153< / span > < span class = "comment" > // Appends all rows from the given matrix to the calling object after the last< / span > < / div >
< div class = "line" > < a id = "l00154" name = "l00154" > < / a > < span class = "lineno" > 154< / span > < span class = "comment" > // row of the calling object. Both matrices must have the same number of< / span > < / div >
< div class = "line" > < a id = "l00155" name = "l00155" > < / a > < span class = "lineno" > 155< / span > < span class = "comment" > // columns. The method returns true if the rows were added successfully and< / span > < / div >
< div class = "line" > < a id = "l00156" name = "l00156" > < / a > < span class = "lineno" > 156< / span > < span class = "comment" > // false if it can' t add the rows because the number of columns of the< / span > < / div >
< div class = "line" > < a id = "l00157" name = "l00157" > < / a > < span class = "lineno" > 157< / span > < span class = "comment" > // matrices are different.< / span > < / div >
< div class = "line" > < a id = "l00158" name = "l00158" > < / a > < span class = "lineno" > 158< / span > < span class = "keywordtype" > bool< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#ab2d8beb101c26b08a8af602300da1748" > AppendRowsFromSparseMatrix< / a > (< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html" > SparseMatrix< / a > & matrix);< / div >
< div class = "line" > < a id = "l00159" name = "l00159" > < / a > < span class = "lineno" > 159< / span > < / div >
< div class = "line" > < a id = "l00160" name = "l00160" > < / a > < span class = "lineno" > 160< / span > < span class = "comment" > // Applies the row permutation.< / span > < / div >
< div class = "line" > < a id = "l00161" name = "l00161" > < / a > < span class = "lineno" > 161< / span > < span class = "keywordtype" > void< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#ace1973900f4d921a7bedbcbe36e1bcad" > ApplyRowPermutation< / a > (< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_permutation.html" > RowPermutation< / a > & row_perm);< / div >
< div class = "line" > < a id = "l00162" name = "l00162" > < / a > < span class = "lineno" > 162< / span > < / div >
< div class = "line" > < a id = "l00163" name = "l00163" > < / a > < span class = "lineno" > 163< / span > < span class = "comment" > // Returns the coefficient at position row in column col.< / span > < / div >
< div class = "line" > < a id = "l00164" name = "l00164" > < / a > < span class = "lineno" > 164< / span > < span class = "comment" > // Call with care: runs in O(num_entries_in_col) as entries may not be sorted.< / span > < / div >
< div class = "line" > < a id = "l00165" name = "l00165" > < / a > < span class = "lineno" > 165< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a566008ab9fd3e3dbec96263bc3c45061" > LookUpValue< / a > (RowIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aea35f36ba98d5bbd8d033382f50c9e52" > row< / a > , ColIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > ) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00166" name = "l00166" > < / a > < span class = "lineno" > 166< / span > < / div >
< div class = "line" > < a id = "l00167" name = "l00167" > < / a > < span class = "lineno" > 167< / span > < span class = "comment" > // Returns true if the matrix equals a (with a maximum error smaller than< / span > < / div >
< div class = "line" > < a id = "l00168" name = "l00168" > < / a > < span class = "lineno" > 168< / span > < span class = "comment" > // given the tolerance).< / span > < / div >
< div class = "line" > < a id = "l00169" name = "l00169" > < / a > < span class = "lineno" > 169< / span > < span class = "keywordtype" > bool< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#af782744bdb01cf56841a0de18ccca3ce" > Equals< / a > (< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html" > SparseMatrix< / a > & < a class = "code hl_variable" href = "constraint__solver_2table_8cc.html#acb18315d548212835cd8ed4287e6c0b6" > a< / a > , < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > tolerance) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00170" name = "l00170" > < / a > < span class = "lineno" > 170< / span > < / div >
< div class = "line" > < a id = "l00171" name = "l00171" > < / a > < span class = "lineno" > 171< / span > < span class = "comment" > // Returns, in min_magnitude and max_magnitude, the minimum and maximum< / span > < / div >
< div class = "line" > < a id = "l00172" name = "l00172" > < / a > < span class = "lineno" > 172< / span > < span class = "comment" > // magnitudes of the non-zero coefficients of the calling object.< / span > < / div >
< div class = "line" > < a id = "l00173" name = "l00173" > < / a > < span class = "lineno" > 173< / span > < span class = "keywordtype" > void< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a6ae7b0836055b9b6d182115027d496f9" > ComputeMinAndMaxMagnitudes< / a > (< a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > * min_magnitude,< / div >
< div class = "line" > < a id = "l00174" name = "l00174" > < / a > < span class = "lineno" > 174< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > * max_magnitude) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00175" name = "l00175" > < / a > < span class = "lineno" > 175< / span > < / div >
< div class = "line" > < a id = "l00176" name = "l00176" > < / a > < span class = "lineno" > 176< / span > < span class = "comment" > // Return the matrix dimension.< / span > < / div >
< div class = "line" > < a id = "l00177" name = "l00177" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a960110e64357a3e69162ebf1f71959dd" > 177< / a > < / span > RowIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a960110e64357a3e69162ebf1f71959dd" > num_rows< / a > ()< span class = "keyword" > const < / span > { < span class = "keywordflow" > return< / span > num_rows_; }< / div >
< div class = "line" > < a id = "l00178" name = "l00178" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a41741829541d089f1c4d34f190884813" > 178< / a > < / span > ColIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a41741829541d089f1c4d34f190884813" > num_cols< / a > ()< span class = "keyword" > const < / span > { < span class = "keywordflow" > return< / span > ColIndex(columns_.size()); }< / div >
< div class = "line" > < a id = "l00179" name = "l00179" > < / a > < span class = "lineno" > 179< / span > < / div >
< div class = "line" > < a id = "l00180" name = "l00180" > < / a > < span class = "lineno" > 180< / span > < span class = "comment" > // Access the underlying sparse columns.< / span > < / div >
< div class = "line" > < a id = "l00181" name = "l00181" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a7273cc492a51a1c5d45c620b32fce502" > 181< / a > < / span > < span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_column.html" > SparseColumn< / a > & < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a7273cc492a51a1c5d45c620b32fce502" > column< / a > (ColIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > )< span class = "keyword" > const < / span > { < span class = "keywordflow" > return< / span > columns_[< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > ]; }< / div >
< div class = "line" > < a id = "l00182" name = "l00182" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a564caf35589006190ef4985fbda74faa" > 182< / a > < / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_column.html" > SparseColumn< / a > * < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a564caf35589006190ef4985fbda74faa" > mutable_column< / a > (ColIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > ) { < span class = "keywordflow" > return< / span > & (columns_[< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > ]); }< / div >
< div class = "line" > < a id = "l00183" name = "l00183" > < / a > < span class = "lineno" > 183< / span > < / div >
< div class = "line" > < a id = "l00184" name = "l00184" > < / a > < span class = "lineno" > 184< / span > < span class = "comment" > // Returns the total numbers of entries in the matrix.< / span > < / div >
< div class = "line" > < a id = "l00185" name = "l00185" > < / a > < span class = "lineno" > 185< / span > < span class = "comment" > // Runs in O(num_cols).< / span > < / div >
< div class = "line" > < a id = "l00186" name = "l00186" > < / a > < span class = "lineno" > 186< / span > EntryIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#af69d9b7065a8f31604a8134be4307749" > num_entries< / a > () < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00187" name = "l00187" > < / a > < span class = "lineno" > 187< / span > < / div >
< div class = "line" > < a id = "l00188" name = "l00188" > < / a > < span class = "lineno" > 188< / span > < span class = "comment" > // Computes the 1-norm of the matrix.< / span > < / div >
< div class = "line" > < a id = "l00189" name = "l00189" > < / a > < span class = "lineno" > 189< / span > < span class = "comment" > // The 1-norm |A| is defined as max_j sum_i |a_ij| or< / span > < / div >
< div class = "line" > < a id = "l00190" name = "l00190" > < / a > < span class = "lineno" > 190< / span > < span class = "comment" > // max_col sum_row |a(row,col)|.< / span > < / div >
< div class = "line" > < a id = "l00191" name = "l00191" > < / a > < span class = "lineno" > 191< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a64fea3282d498f3eb2d4af70692bb117" > ComputeOneNorm< / a > () < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00192" name = "l00192" > < / a > < span class = "lineno" > 192< / span > < / div >
< div class = "line" > < a id = "l00193" name = "l00193" > < / a > < span class = "lineno" > 193< / span > < span class = "comment" > // Computes the oo-norm (infinity-norm) of the matrix.< / span > < / div >
< div class = "line" > < a id = "l00194" name = "l00194" > < / a > < span class = "lineno" > 194< / span > < span class = "comment" > // The oo-norm |A| is defined as max_i sum_j |a_ij| or< / span > < / div >
< div class = "line" > < a id = "l00195" name = "l00195" > < / a > < span class = "lineno" > 195< / span > < span class = "comment" > // max_row sum_col |a(row,col)|.< / span > < / div >
< div class = "line" > < a id = "l00196" name = "l00196" > < / a > < span class = "lineno" > 196< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a3f219a081f88c22ae282ada4f0bdddd3" > ComputeInfinityNorm< / a > () < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00197" name = "l00197" > < / a > < span class = "lineno" > 197< / span > < / div >
< div class = "line" > < a id = "l00198" name = "l00198" > < / a > < span class = "lineno" > 198< / span > < span class = "comment" > // Returns a dense representation of the matrix.< / span > < / div >
< div class = "line" > < a id = "l00199" name = "l00199" > < / a > < span class = "lineno" > 199< / span > std::string < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#ab4a8456683d4572bd9426efab8489e99" > Dump< / a > () < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00200" name = "l00200" > < / a > < span class = "lineno" > 200< / span > < / div >
< div class = "line" > < a id = "l00201" name = "l00201" > < / a > < span class = "lineno" > 201< / span > < span class = "keyword" > private< / span > :< / div >
< div class = "line" > < a id = "l00202" name = "l00202" > < / a > < span class = "lineno" > 202< / span > < span class = "comment" > // Resets the internal data structure and create an empty rectangular< / span > < / div >
< div class = "line" > < a id = "l00203" name = "l00203" > < / a > < span class = "lineno" > 203< / span > < span class = "comment" > // matrix of size num_rows x num_cols.< / span > < / div >
< div class = "line" > < a id = "l00204" name = "l00204" > < / a > < span class = "lineno" > 204< / span > < span class = "keywordtype" > void< / span > Reset(ColIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a41741829541d089f1c4d34f190884813" > num_cols< / a > , RowIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a960110e64357a3e69162ebf1f71959dd" > num_rows< / a > );< / div >
< div class = "line" > < a id = "l00205" name = "l00205" > < / a > < span class = "lineno" > 205< / span > < / div >
< div class = "line" > < a id = "l00206" name = "l00206" > < / a > < span class = "lineno" > 206< / span > < span class = "comment" > // Vector of sparse columns.< / span > < / div >
< div class = "line" > < a id = "l00207" name = "l00207" > < / a > < span class = "lineno" > 207< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > StrictITIVector< ColIndex, SparseColumn> < / a > columns_;< / div >
< div class = "line" > < a id = "l00208" name = "l00208" > < / a > < span class = "lineno" > 208< / span > < / div >
< div class = "line" > < a id = "l00209" name = "l00209" > < / a > < span class = "lineno" > 209< / span > < span class = "comment" > // Number of rows. This is needed as sparse columns don' t have a maximum< / span > < / div >
< div class = "line" > < a id = "l00210" name = "l00210" > < / a > < span class = "lineno" > 210< / span > < span class = "comment" > // number of rows.< / span > < / div >
< div class = "line" > < a id = "l00211" name = "l00211" > < / a > < span class = "lineno" > 211< / span > RowIndex num_rows_;< / div >
< div class = "line" > < a id = "l00212" name = "l00212" > < / a > < span class = "lineno" > 212< / span > < / div >
< div class = "line" > < a id = "l00213" name = "l00213" > < / a > < span class = "lineno" > 213< / span > DISALLOW_COPY_AND_ASSIGN(< a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html" > SparseMatrix< / a > );< / div >
< div class = "line" > < a id = "l00214" name = "l00214" > < / a > < span class = "lineno" > 214< / span > };< / div >
< div class = "line" > < a id = "l00215" name = "l00215" > < / a > < span class = "lineno" > 215< / span > < / div >
< div class = "line" > < a id = "l00216" name = "l00216" > < / a > < span class = "lineno" > 216< / span > < span class = "comment" > // A matrix constructed from a list of already existing SparseColumn. This class< / span > < / div >
< div class = "line" > < a id = "l00217" name = "l00217" > < / a > < span class = "lineno" > 217< / span > < span class = "comment" > // does not take ownership of the underlying columns, and thus they must outlive< / span > < / div >
< div class = "line" > < a id = "l00218" name = "l00218" > < / a > < span class = "lineno" > 218< / span > < span class = "comment" > // this class (and keep the same address in memory).< / span > < / div >
< div class = "line" > < a id = "l00219" name = "l00219" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_matrix_view.html" > 219< / a > < / span > < span class = "keyword" > class < / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_matrix_view.html" > MatrixView< / a > {< / div >
< div class = "line" > < a id = "l00220" name = "l00220" > < / a > < span class = "lineno" > 220< / span > < span class = "keyword" > public< / span > :< / div >
< div class = "line" > < a id = "l00221" name = "l00221" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_matrix_view.html#a1f0797ca04f7cb50938328e7e027a18b" > 221< / a > < / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_matrix_view.html#a1f0797ca04f7cb50938328e7e027a18b" > MatrixView< / a > () {}< / div >
< div class = "line" > < a id = "l00222" name = "l00222" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_matrix_view.html#ac220f9bed6efccb65c2514e90d702638" > 222< / a > < / span > < span class = "keyword" > explicit< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_matrix_view.html#ac220f9bed6efccb65c2514e90d702638" > MatrixView< / a > (< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html" > SparseMatrix< / a > & matrix) {< / div >
< div class = "line" > < a id = "l00223" name = "l00223" > < / a > < span class = "lineno" > 223< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_matrix_view.html#a495dfea7028bd3b07c1485d5c66b7001" > PopulateFromMatrix< / a > (matrix);< / div >
< div class = "line" > < a id = "l00224" name = "l00224" > < / a > < span class = "lineno" > 224< / span > }< / div >
< div class = "line" > < a id = "l00225" name = "l00225" > < / a > < span class = "lineno" > 225< / span > < / div >
< div class = "line" > < a id = "l00226" name = "l00226" > < / a > < span class = "lineno" > 226< / span > < span class = "comment" > // Takes all the columns of the given matrix.< / span > < / div >
< div class = "line" > < a id = "l00227" name = "l00227" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_matrix_view.html#a495dfea7028bd3b07c1485d5c66b7001" > 227< / a > < / span > < span class = "keywordtype" > void< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_matrix_view.html#a495dfea7028bd3b07c1485d5c66b7001" > PopulateFromMatrix< / a > (< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html" > SparseMatrix< / a > & matrix) {< / div >
< div class = "line" > < a id = "l00228" name = "l00228" > < / a > < span class = "lineno" > 228< / span > < span class = "keyword" > const< / span > ColIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_matrix_view.html#a41741829541d089f1c4d34f190884813" > num_cols< / a > = matrix.< a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a41741829541d089f1c4d34f190884813" > num_cols< / a > ();< / div >
< div class = "line" > < a id = "l00229" name = "l00229" > < / a > < span class = "lineno" > 229< / span > columns_.resize(< a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_matrix_view.html#a41741829541d089f1c4d34f190884813" > num_cols< / a > , < span class = "keyword" > nullptr< / span > );< / div >
< div class = "line" > < a id = "l00230" name = "l00230" > < / a > < span class = "lineno" > 230< / span > < span class = "keywordflow" > for< / span > (ColIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > (0); < a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > < < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_matrix_view.html#a41741829541d089f1c4d34f190884813" > num_cols< / a > ; ++< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > ) {< / div >
< div class = "line" > < a id = "l00231" name = "l00231" > < / a > < span class = "lineno" > 231< / span > columns_[< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > ] = & matrix.< a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a7273cc492a51a1c5d45c620b32fce502" > column< / a > (< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > );< / div >
< div class = "line" > < a id = "l00232" name = "l00232" > < / a > < span class = "lineno" > 232< / span > }< / div >
< div class = "line" > < a id = "l00233" name = "l00233" > < / a > < span class = "lineno" > 233< / span > num_rows_ = matrix.< a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a960110e64357a3e69162ebf1f71959dd" > num_rows< / a > ();< / div >
< div class = "line" > < a id = "l00234" name = "l00234" > < / a > < span class = "lineno" > 234< / span > }< / div >
< div class = "line" > < a id = "l00235" name = "l00235" > < / a > < span class = "lineno" > 235< / span > < / div >
< div class = "line" > < a id = "l00236" name = "l00236" > < / a > < span class = "lineno" > 236< / span > < span class = "comment" > // Takes all the columns of the first matrix followed by the columns of the< / span > < / div >
< div class = "line" > < a id = "l00237" name = "l00237" > < / a > < span class = "lineno" > 237< / span > < span class = "comment" > // second matrix.< / span > < / div >
< div class = "line" > < a id = "l00238" name = "l00238" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_matrix_view.html#a87c606b7a9b920de2d4b6aa5c3bc1a45" > 238< / a > < / span > < span class = "keywordtype" > void< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_matrix_view.html#a87c606b7a9b920de2d4b6aa5c3bc1a45" > PopulateFromMatrixPair< / a > (< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html" > SparseMatrix< / a > & matrix_a,< / div >
< div class = "line" > < a id = "l00239" name = "l00239" > < / a > < span class = "lineno" > 239< / span > < span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html" > SparseMatrix< / a > & matrix_b) {< / div >
< div class = "line" > < a id = "l00240" name = "l00240" > < / a > < span class = "lineno" > 240< / span > < span class = "keyword" > const< / span > ColIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_matrix_view.html#a41741829541d089f1c4d34f190884813" > num_cols< / a > = matrix_a.< a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a41741829541d089f1c4d34f190884813" > num_cols< / a > () + matrix_b.< a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a41741829541d089f1c4d34f190884813" > num_cols< / a > ();< / div >
< div class = "line" > < a id = "l00241" name = "l00241" > < / a > < span class = "lineno" > 241< / span > columns_.resize(< a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_matrix_view.html#a41741829541d089f1c4d34f190884813" > num_cols< / a > , < span class = "keyword" > nullptr< / span > );< / div >
< div class = "line" > < a id = "l00242" name = "l00242" > < / a > < span class = "lineno" > 242< / span > < span class = "keywordflow" > for< / span > (ColIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > (0); < a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > < matrix_a.< a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a41741829541d089f1c4d34f190884813" > num_cols< / a > (); ++< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > ) {< / div >
< div class = "line" > < a id = "l00243" name = "l00243" > < / a > < span class = "lineno" > 243< / span > columns_[< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > ] = & matrix_a.< a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a7273cc492a51a1c5d45c620b32fce502" > column< / a > (< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > );< / div >
< div class = "line" > < a id = "l00244" name = "l00244" > < / a > < span class = "lineno" > 244< / span > }< / div >
< div class = "line" > < a id = "l00245" name = "l00245" > < / a > < span class = "lineno" > 245< / span > < span class = "keywordflow" > for< / span > (ColIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > (0); < a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > < matrix_b.< a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a41741829541d089f1c4d34f190884813" > num_cols< / a > (); ++< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > ) {< / div >
< div class = "line" > < a id = "l00246" name = "l00246" > < / a > < span class = "lineno" > 246< / span > columns_[matrix_a.< a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a41741829541d089f1c4d34f190884813" > num_cols< / a > () + < a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > ] = & matrix_b.< a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a7273cc492a51a1c5d45c620b32fce502" > column< / a > (< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > );< / div >
< div class = "line" > < a id = "l00247" name = "l00247" > < / a > < span class = "lineno" > 247< / span > }< / div >
< div class = "line" > < a id = "l00248" name = "l00248" > < / a > < span class = "lineno" > 248< / span > num_rows_ = < a class = "code hl_variable" href = "alldiff__cst_8cc.html#a26e6db9bcc64b584051ecc28171ed11f" > std::max< / a > (matrix_a.< a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a960110e64357a3e69162ebf1f71959dd" > num_rows< / a > (), matrix_b.< a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a960110e64357a3e69162ebf1f71959dd" > num_rows< / a > ());< / div >
< div class = "line" > < a id = "l00249" name = "l00249" > < / a > < span class = "lineno" > 249< / span > }< / div >
< div class = "line" > < a id = "l00250" name = "l00250" > < / a > < span class = "lineno" > 250< / span > < / div >
< div class = "line" > < a id = "l00251" name = "l00251" > < / a > < span class = "lineno" > 251< / span > < span class = "comment" > // Takes only the columns of the given matrix that belongs to the given basis.< / span > < / div >
< div class = "line" > < a id = "l00252" name = "l00252" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_matrix_view.html#adc9aa1d344fe9442ac3ba673b939db7c" > 252< / a > < / span > < span class = "keywordtype" > void< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_matrix_view.html#adc9aa1d344fe9442ac3ba673b939db7c" > PopulateFromBasis< / a > (< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_matrix_view.html" > MatrixView< / a > & matrix,< / div >
< div class = "line" > < a id = "l00253" name = "l00253" > < / a > < span class = "lineno" > 253< / span > < span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > RowToColMapping< / a > & basis) {< / div >
< div class = "line" > < a id = "l00254" name = "l00254" > < / a > < span class = "lineno" > 254< / span > columns_.resize(< a class = "code hl_function" href = "namespaceoperations__research_1_1glop.html#a8fbc9efd86a3cc862a9079d86ab8b524" > RowToColIndex< / a > (basis.< a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html#a967a5c081ad4195a30c78dc2c0bcabf5" > size< / a > ()), < span class = "keyword" > nullptr< / span > );< / div >
< div class = "line" > < a id = "l00255" name = "l00255" > < / a > < span class = "lineno" > 255< / span > < span class = "keywordflow" > for< / span > (RowIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aea35f36ba98d5bbd8d033382f50c9e52" > row< / a > (0); < a class = "code hl_variable" href = "markowitz_8cc.html#aea35f36ba98d5bbd8d033382f50c9e52" > row< / a > < basis.< a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html#a967a5c081ad4195a30c78dc2c0bcabf5" > size< / a > (); ++< a class = "code hl_variable" href = "markowitz_8cc.html#aea35f36ba98d5bbd8d033382f50c9e52" > row< / a > ) {< / div >
< div class = "line" > < a id = "l00256" name = "l00256" > < / a > < span class = "lineno" > 256< / span > columns_[< a class = "code hl_function" href = "namespaceoperations__research_1_1glop.html#a8fbc9efd86a3cc862a9079d86ab8b524" > RowToColIndex< / a > (< a class = "code hl_variable" href = "markowitz_8cc.html#aea35f36ba98d5bbd8d033382f50c9e52" > row< / a > )] = & matrix.< a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_matrix_view.html#a7273cc492a51a1c5d45c620b32fce502" > column< / a > (basis[< a class = "code hl_variable" href = "markowitz_8cc.html#aea35f36ba98d5bbd8d033382f50c9e52" > row< / a > ]);< / div >
< div class = "line" > < a id = "l00257" name = "l00257" > < / a > < span class = "lineno" > 257< / span > }< / div >
< div class = "line" > < a id = "l00258" name = "l00258" > < / a > < span class = "lineno" > 258< / span > num_rows_ = matrix.< a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_matrix_view.html#a960110e64357a3e69162ebf1f71959dd" > num_rows< / a > ();< / div >
< div class = "line" > < a id = "l00259" name = "l00259" > < / a > < span class = "lineno" > 259< / span > }< / div >
< div class = "line" > < a id = "l00260" name = "l00260" > < / a > < span class = "lineno" > 260< / span > < / div >
< div class = "line" > < a id = "l00261" name = "l00261" > < / a > < span class = "lineno" > 261< / span > < span class = "comment" > // Same behavior as the SparseMatrix functions above.< / span > < / div >
< div class = "line" > < a id = "l00262" name = "l00262" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_matrix_view.html#a8e12342fc420701fbffd97025421575a" > 262< / a > < / span > < span class = "keywordtype" > bool< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_matrix_view.html#a8e12342fc420701fbffd97025421575a" > IsEmpty< / a > ()< span class = "keyword" > const < / span > { < span class = "keywordflow" > return< / span > columns_.empty(); }< / div >
< div class = "line" > < a id = "l00263" name = "l00263" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_matrix_view.html#a960110e64357a3e69162ebf1f71959dd" > 263< / a > < / span > RowIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_matrix_view.html#a960110e64357a3e69162ebf1f71959dd" > num_rows< / a > ()< span class = "keyword" > const < / span > { < span class = "keywordflow" > return< / span > num_rows_; }< / div >
< div class = "line" > < a id = "l00264" name = "l00264" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_matrix_view.html#a41741829541d089f1c4d34f190884813" > 264< / a > < / span > ColIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_matrix_view.html#a41741829541d089f1c4d34f190884813" > num_cols< / a > ()< span class = "keyword" > const < / span > { < span class = "keywordflow" > return< / span > columns_.size(); }< / div >
< div class = "line" > < a id = "l00265" name = "l00265" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_matrix_view.html#a7273cc492a51a1c5d45c620b32fce502" > 265< / a > < / span > < span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_column.html" > SparseColumn< / a > & < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_matrix_view.html#a7273cc492a51a1c5d45c620b32fce502" > column< / a > (ColIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > )< span class = "keyword" > const < / span > { < span class = "keywordflow" > return< / span > *columns_[< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > ]; }< / div >
< div class = "line" > < a id = "l00266" name = "l00266" > < / a > < span class = "lineno" > 266< / span > EntryIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_matrix_view.html#af69d9b7065a8f31604a8134be4307749" > num_entries< / a > () < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00267" name = "l00267" > < / a > < span class = "lineno" > 267< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_matrix_view.html#a64fea3282d498f3eb2d4af70692bb117" > ComputeOneNorm< / a > () < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00268" name = "l00268" > < / a > < span class = "lineno" > 268< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_matrix_view.html#a3f219a081f88c22ae282ada4f0bdddd3" > ComputeInfinityNorm< / a > () < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00269" name = "l00269" > < / a > < span class = "lineno" > 269< / span > < / div >
< div class = "line" > < a id = "l00270" name = "l00270" > < / a > < span class = "lineno" > 270< / span > < span class = "keyword" > private< / span > :< / div >
< div class = "line" > < a id = "l00271" name = "l00271" > < / a > < span class = "lineno" > 271< / span > RowIndex num_rows_;< / div >
< div class = "line" > < a id = "l00272" name = "l00272" > < / a > < span class = "lineno" > 272< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > StrictITIVector< ColIndex, SparseColumn const*> < / a > columns_;< / div >
< div class = "line" > < a id = "l00273" name = "l00273" > < / a > < span class = "lineno" > 273< / span > };< / div >
< div class = "line" > < a id = "l00274" name = "l00274" > < / a > < span class = "lineno" > 274< / span > < / div >
< div class = "line" > < a id = "l00275" name = "l00275" > < / a > < span class = "lineno" > 275< / span > < span class = "keyword" > extern< / span > < span class = "keyword" > template< / span > < span class = "keywordtype" > void< / span > SparseMatrix::PopulateFromTranspose< SparseMatrix> (< / div >
< div class = "line" > < a id = "l00276" name = "l00276" > < / a > < span class = "lineno" > 276< / span > < span class = "keyword" > const< / span > SparseMatrix& < a class = "code hl_function" href = "parser_8yy_8cc.html#a5a634cf4429798b1c921a81de8250051" > input< / a > );< / div >
< div class = "line" > < a id = "l00277" name = "l00277" > < / a > < span class = "lineno" > 277< / span > < span class = "keyword" > extern< / span > < span class = "keyword" > template< / span > < span class = "keywordtype" > void< / span > SparseMatrix::PopulateFromPermutedMatrix< SparseMatrix> (< / div >
< div class = "line" > < a id = "l00278" name = "l00278" > < / a > < span class = "lineno" > 278< / span > < span class = "keyword" > const< / span > SparseMatrix& < a class = "code hl_variable" href = "constraint__solver_2table_8cc.html#acb18315d548212835cd8ed4287e6c0b6" > a< / a > , < span class = "keyword" > const< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#ae69267cf0653a77925ee13121b9857ec" > RowPermutation< / a > & row_perm,< / div >
< div class = "line" > < a id = "l00279" name = "l00279" > < / a > < span class = "lineno" > 279< / span > < span class = "keyword" > const< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a6b1b56ad0cb77edbd314f2bec33b467a" > ColumnPermutation< / a > & inverse_col_perm);< / div >
< div class = "line" > < a id = "l00280" name = "l00280" > < / a > < span class = "lineno" > 280< / span > < span class = "keyword" > extern< / span > < span class = "keyword" > template< / span > < span class = "keywordtype" > void< / span > < / div >
< div class = "line" > < a id = "l00281" name = "l00281" > < / a > < span class = "lineno" > 281< / span > SparseMatrix::PopulateFromPermutedMatrix< CompactSparseMatrixView> (< / div >
< div class = "line" > < a id = "l00282" name = "l00282" > < / a > < span class = "lineno" > 282< / span > < span class = "keyword" > const< / span > CompactSparseMatrixView& < a class = "code hl_variable" href = "constraint__solver_2table_8cc.html#acb18315d548212835cd8ed4287e6c0b6" > a< / a > , < span class = "keyword" > const< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#ae69267cf0653a77925ee13121b9857ec" > RowPermutation< / a > & row_perm,< / div >
< div class = "line" > < a id = "l00283" name = "l00283" > < / a > < span class = "lineno" > 283< / span > < span class = "keyword" > const< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a6b1b56ad0cb77edbd314f2bec33b467a" > ColumnPermutation< / a > & inverse_col_perm);< / div >
< div class = "line" > < a id = "l00284" name = "l00284" > < / a > < span class = "lineno" > 284< / span > < / div >
< div class = "line" > < a id = "l00285" name = "l00285" > < / a > < span class = "lineno" > 285< / span > < span class = "comment" > // Another matrix representation which is more efficient than a SparseMatrix but< / span > < / div >
< div class = "line" > < a id = "l00286" name = "l00286" > < / a > < span class = "lineno" > 286< / span > < span class = "comment" > // doesn' t allow matrix modification. It is faster to construct, uses less< / span > < / div >
< div class = "line" > < a id = "l00287" name = "l00287" > < / a > < span class = "lineno" > 287< / span > < span class = "comment" > // memory and provides a better cache locality when iterating over the non-zeros< / span > < / div >
< div class = "line" > < a id = "l00288" name = "l00288" > < / a > < span class = "lineno" > 288< / span > < span class = "comment" > // of the matrix columns.< / span > < / div >
< div class = "line" > < a id = "l00289" name = "l00289" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html" > 289< / a > < / span > < span class = "keyword" > class < / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html" > CompactSparseMatrix< / a > {< / div >
< div class = "line" > < a id = "l00290" name = "l00290" > < / a > < span class = "lineno" > 290< / span > < span class = "keyword" > public< / span > :< / div >
< div class = "line" > < a id = "l00291" name = "l00291" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a319ffa92d03907ee98b5f3da18421af3" > 291< / a > < / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a319ffa92d03907ee98b5f3da18421af3" > CompactSparseMatrix< / a > () {}< / div >
< div class = "line" > < a id = "l00292" name = "l00292" > < / a > < span class = "lineno" > 292< / span > < / div >
< div class = "line" > < a id = "l00293" name = "l00293" > < / a > < span class = "lineno" > 293< / span > < span class = "comment" > // Convenient constructors for tests.< / span > < / div >
< div class = "line" > < a id = "l00294" name = "l00294" > < / a > < span class = "lineno" > 294< / span > < span class = "comment" > // TODO(user): If this is needed in production code, it can be done faster.< / span > < / div >
< div class = "line" > < a id = "l00295" name = "l00295" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a9f271f559e0d1e794a2ecc76d919db68" > 295< / a > < / span > < span class = "keyword" > explicit< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a9f271f559e0d1e794a2ecc76d919db68" > CompactSparseMatrix< / a > (< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html" > SparseMatrix< / a > & matrix) {< / div >
< div class = "line" > < a id = "l00296" name = "l00296" > < / a > < span class = "lineno" > 296< / span > PopulateFromMatrixView(< a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_matrix_view.html" > MatrixView< / a > (matrix));< / div >
< div class = "line" > < a id = "l00297" name = "l00297" > < / a > < span class = "lineno" > 297< / span > }< / div >
< div class = "line" > < a id = "l00298" name = "l00298" > < / a > < span class = "lineno" > 298< / span > < / div >
< div class = "line" > < a id = "l00299" name = "l00299" > < / a > < span class = "lineno" > 299< / span > < span class = "comment" > // Creates a CompactSparseMatrix from the given MatrixView. The matrices are< / span > < / div >
< div class = "line" > < a id = "l00300" name = "l00300" > < / a > < span class = "lineno" > 300< / span > < span class = "comment" > // the same, only the representation differ. Note that the entry order in< / span > < / div >
< div class = "line" > < a id = "l00301" name = "l00301" > < / a > < span class = "lineno" > 301< / span > < span class = "comment" > // each column is preserved.< / span > < / div >
< div class = "line" > < a id = "l00302" name = "l00302" > < / a > < span class = "lineno" > 302< / span > < span class = "keywordtype" > void< / span > PopulateFromMatrixView(< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_matrix_view.html" > MatrixView< / a > & < a class = "code hl_function" href = "parser_8yy_8cc.html#a5a634cf4429798b1c921a81de8250051" > input< / a > );< / div >
< div class = "line" > < a id = "l00303" name = "l00303" > < / a > < span class = "lineno" > 303< / span > < / div >
< div class = "line" > < a id = "l00304" name = "l00304" > < / a > < span class = "lineno" > 304< / span > < span class = "comment" > // Creates a CompactSparseMatrix by copying the input and adding an identity< / span > < / div >
< div class = "line" > < a id = "l00305" name = "l00305" > < / a > < span class = "lineno" > 305< / span > < span class = "comment" > // matrix to the left of it.< / span > < / div >
< div class = "line" > < a id = "l00306" name = "l00306" > < / a > < span class = "lineno" > 306< / span > < span class = "keywordtype" > void< / span > PopulateFromSparseMatrixAndAddSlacks(< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html" > SparseMatrix< / a > & < a class = "code hl_function" href = "parser_8yy_8cc.html#a5a634cf4429798b1c921a81de8250051" > input< / a > );< / div >
< div class = "line" > < a id = "l00307" name = "l00307" > < / a > < span class = "lineno" > 307< / span > < / div >
< div class = "line" > < a id = "l00308" name = "l00308" > < / a > < span class = "lineno" > 308< / span > < span class = "comment" > // Creates a CompactSparseMatrix from the transpose of the given< / span > < / div >
< div class = "line" > < a id = "l00309" name = "l00309" > < / a > < span class = "lineno" > 309< / span > < span class = "comment" > // CompactSparseMatrix. Note that the entries in each columns will be ordered< / span > < / div >
< div class = "line" > < a id = "l00310" name = "l00310" > < / a > < span class = "lineno" > 310< / span > < span class = "comment" > // by row indices.< / span > < / div >
< div class = "line" > < a id = "l00311" name = "l00311" > < / a > < span class = "lineno" > 311< / span > < span class = "keywordtype" > void< / span > PopulateFromTranspose(< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html" > CompactSparseMatrix< / a > & < a class = "code hl_function" href = "parser_8yy_8cc.html#a5a634cf4429798b1c921a81de8250051" > input< / a > );< / div >
< div class = "line" > < a id = "l00312" name = "l00312" > < / a > < span class = "lineno" > 312< / span > < / div >
< div class = "line" > < a id = "l00313" name = "l00313" > < / a > < span class = "lineno" > 313< / span > < span class = "comment" > // Clears the matrix and sets its number of rows. If none of the Populate()< / span > < / div >
< div class = "line" > < a id = "l00314" name = "l00314" > < / a > < span class = "lineno" > 314< / span > < span class = "comment" > // function has been called, Reset() must be called before calling any of the< / span > < / div >
< div class = "line" > < a id = "l00315" name = "l00315" > < / a > < span class = "lineno" > 315< / span > < span class = "comment" > // Add*() functions below.< / span > < / div >
< div class = "line" > < a id = "l00316" name = "l00316" > < / a > < span class = "lineno" > 316< / span > < span class = "keywordtype" > void< / span > Reset(RowIndex num_rows);< / div >
< div class = "line" > < a id = "l00317" name = "l00317" > < / a > < span class = "lineno" > 317< / span > < / div >
< div class = "line" > < a id = "l00318" name = "l00318" > < / a > < span class = "lineno" > 318< / span > < span class = "comment" > // Adds a dense column to the CompactSparseMatrix (only the non-zero will be< / span > < / div >
< div class = "line" > < a id = "l00319" name = "l00319" > < / a > < span class = "lineno" > 319< / span > < span class = "comment" > // actually stored). This work in O(input.size()) and returns the index of the< / span > < / div >
< div class = "line" > < a id = "l00320" name = "l00320" > < / a > < span class = "lineno" > 320< / span > < span class = "comment" > // added column.< / span > < / div >
< div class = "line" > < a id = "l00321" name = "l00321" > < / a > < span class = "lineno" > 321< / span > ColIndex AddDenseColumn(< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > & dense_column);< / div >
< div class = "line" > < a id = "l00322" name = "l00322" > < / a > < span class = "lineno" > 322< / span > < / div >
< div class = "line" > < a id = "l00323" name = "l00323" > < / a > < span class = "lineno" > 323< / span > < span class = "comment" > // Same as AddDenseColumn(), but only adds the non-zero from the given start.< / span > < / div >
< div class = "line" > < a id = "l00324" name = "l00324" > < / a > < span class = "lineno" > 324< / span > ColIndex AddDenseColumnPrefix(< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > & dense_column,< / div >
< div class = "line" > < a id = "l00325" name = "l00325" > < / a > < span class = "lineno" > 325< / span > RowIndex start);< / div >
< div class = "line" > < a id = "l00326" name = "l00326" > < / a > < span class = "lineno" > 326< / span > < / div >
< div class = "line" > < a id = "l00327" name = "l00327" > < / a > < span class = "lineno" > 327< / span > < span class = "comment" > // Same as AddDenseColumn(), but uses the given non_zeros pattern of input.< / span > < / div >
< div class = "line" > < a id = "l00328" name = "l00328" > < / a > < span class = "lineno" > 328< / span > < span class = "comment" > // If non_zeros is empty, this actually calls AddDenseColumn().< / span > < / div >
< div class = "line" > < a id = "l00329" name = "l00329" > < / a > < span class = "lineno" > 329< / span > ColIndex AddDenseColumnWithNonZeros(< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > & dense_column,< / div >
< div class = "line" > < a id = "l00330" name = "l00330" > < / a > < span class = "lineno" > 330< / span > < span class = "keyword" > const< / span > std::vector< RowIndex> & non_zeros);< / div >
< div class = "line" > < a id = "l00331" name = "l00331" > < / a > < span class = "lineno" > 331< / span > < / div >
< div class = "line" > < a id = "l00332" name = "l00332" > < / a > < span class = "lineno" > 332< / span > < span class = "comment" > // Adds a dense column for which we know the non-zero positions and clears it.< / span > < / div >
< div class = "line" > < a id = "l00333" name = "l00333" > < / a > < span class = "lineno" > 333< / span > < span class = "comment" > // Note that this function supports duplicate indices in non_zeros. The< / span > < / div >
< div class = "line" > < a id = "l00334" name = "l00334" > < / a > < span class = "lineno" > 334< / span > < span class = "comment" > // complexity is in O(non_zeros.size()). Only the indices present in non_zeros< / span > < / div >
< div class = "line" > < a id = "l00335" name = "l00335" > < / a > < span class = "lineno" > 335< / span > < span class = "comment" > // will be cleared. Returns the index of the added column.< / span > < / div >
< div class = "line" > < a id = "l00336" name = "l00336" > < / a > < span class = "lineno" > 336< / span > ColIndex AddAndClearColumnWithNonZeros(< a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > * column,< / div >
< div class = "line" > < a id = "l00337" name = "l00337" > < / a > < span class = "lineno" > 337< / span > std::vector< RowIndex> * non_zeros);< / div >
< div class = "line" > < a id = "l00338" name = "l00338" > < / a > < span class = "lineno" > 338< / span > < / div >
< div class = "line" > < a id = "l00339" name = "l00339" > < / a > < span class = "lineno" > 339< / span > < span class = "comment" > // Returns the number of entries (i.e. degree) of the given column.< / span > < / div >
< div class = "line" > < a id = "l00340" name = "l00340" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#afe3d36f3ba4f04442fbb36f8726f8baf" > 340< / a > < / span > EntryIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#afe3d36f3ba4f04442fbb36f8726f8baf" > ColumnNumEntries< / a > (ColIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > )< span class = "keyword" > const < / span > {< / div >
< div class = "line" > < a id = "l00341" name = "l00341" > < / a > < span class = "lineno" > 341< / span > < span class = "keywordflow" > return< / span > starts_[< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > + 1] - starts_[< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > ];< / div >
< div class = "line" > < a id = "l00342" name = "l00342" > < / a > < span class = "lineno" > 342< / span > }< / div >
< div class = "line" > < a id = "l00343" name = "l00343" > < / a > < span class = "lineno" > 343< / span > < / div >
< div class = "line" > < a id = "l00344" name = "l00344" > < / a > < span class = "lineno" > 344< / span > < span class = "comment" > // Returns the matrix dimensions. See same functions in SparseMatrix.< / span > < / div >
< div class = "line" > < a id = "l00345" name = "l00345" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#af69d9b7065a8f31604a8134be4307749" > 345< / a > < / span > EntryIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#af69d9b7065a8f31604a8134be4307749" > num_entries< / a > ()< span class = "keyword" > const < / span > {< / div >
< div class = "line" > < a id = "l00346" name = "l00346" > < / a > < span class = "lineno" > 346< / span > < a class = "code hl_define" href = "base_2logging_8h.html#ae89df3243bbb8341130c7b3f44145ea0" > DCHECK_EQ< / a > (coefficients_.size(), rows_.size());< / div >
< div class = "line" > < a id = "l00347" name = "l00347" > < / a > < span class = "lineno" > 347< / span > < span class = "keywordflow" > return< / span > coefficients_.size();< / div >
< div class = "line" > < a id = "l00348" name = "l00348" > < / a > < span class = "lineno" > 348< / span > }< / div >
< div class = "line" > < a id = "l00349" name = "l00349" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a960110e64357a3e69162ebf1f71959dd" > 349< / a > < / span > RowIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a960110e64357a3e69162ebf1f71959dd" > num_rows< / a > ()< span class = "keyword" > const < / span > { < span class = "keywordflow" > return< / span > num_rows_; }< / div >
< div class = "line" > < a id = "l00350" name = "l00350" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a41741829541d089f1c4d34f190884813" > 350< / a > < / span > ColIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a41741829541d089f1c4d34f190884813" > num_cols< / a > ()< span class = "keyword" > const < / span > { < span class = "keywordflow" > return< / span > num_cols_; }< / div >
< div class = "line" > < a id = "l00351" name = "l00351" > < / a > < span class = "lineno" > 351< / span > < / div >
< div class = "line" > < a id = "l00352" name = "l00352" > < / a > < span class = "lineno" > 352< / span > < span class = "comment" > // Returns whether or not this matrix contains any non-zero entries.< / span > < / div >
< div class = "line" > < a id = "l00353" name = "l00353" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a8e12342fc420701fbffd97025421575a" > 353< / a > < / span > < span class = "keywordtype" > bool< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a8e12342fc420701fbffd97025421575a" > IsEmpty< / a > ()< span class = "keyword" > const < / span > {< / div >
< div class = "line" > < a id = "l00354" name = "l00354" > < / a > < span class = "lineno" > 354< / span > < a class = "code hl_define" href = "base_2logging_8h.html#ae89df3243bbb8341130c7b3f44145ea0" > DCHECK_EQ< / a > (coefficients_.size(), rows_.size());< / div >
< div class = "line" > < a id = "l00355" name = "l00355" > < / a > < span class = "lineno" > 355< / span > < span class = "keywordflow" > return< / span > coefficients_.empty();< / div >
< div class = "line" > < a id = "l00356" name = "l00356" > < / a > < span class = "lineno" > 356< / span > }< / div >
< div class = "line" > < a id = "l00357" name = "l00357" > < / a > < span class = "lineno" > 357< / span > < / div >
< div class = "line" > < a id = "l00358" name = "l00358" > < / a > < span class = "lineno" > 358< / span > < span class = "comment" > // Functions to iterate on the entries of a given column:< / span > < / div >
< div class = "line" > < a id = "l00359" name = "l00359" > < / a > < span class = "lineno" > 359< / span > < span class = "comment" > // for (const EntryIndex i : compact_matrix_.Column(col)) {< / span > < / div >
< div class = "line" > < a id = "l00360" name = "l00360" > < / a > < span class = "lineno" > 360< / span > < span class = "comment" > // const RowIndex row = compact_matrix_.EntryRow(i);< / span > < / div >
< div class = "line" > < a id = "l00361" name = "l00361" > < / a > < span class = "lineno" > 361< / span > < span class = "comment" > // const Fractional coefficient = compact_matrix_.EntryCoefficient(i);< / span > < / div >
< div class = "line" > < a id = "l00362" name = "l00362" > < / a > < span class = "lineno" > 362< / span > < span class = "comment" > // }< / span > < / div >
< div class = "line" > < a id = "l00363" name = "l00363" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#acedaf830dd26be6213e4665f088c5aa4" > 363< / a > < / span > < a class = "code hl_class" href = "classutil_1_1_integer_range.html" > ::util::IntegerRange< EntryIndex> < / a > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#acedaf830dd26be6213e4665f088c5aa4" > Column< / a > (ColIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > )< span class = "keyword" > const < / span > {< / div >
< div class = "line" > < a id = "l00364" name = "l00364" > < / a > < span class = "lineno" > 364< / span > return ::util::IntegerRange< EntryIndex> (starts_[< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > ], starts_[< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > + 1]);< / div >
< div class = "line" > < a id = "l00365" name = "l00365" > < / a > < span class = "lineno" > 365< / span > }< / div >
< div class = "line" > < a id = "l00366" name = "l00366" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#aaef7fc778a29bb3bb3040c0423937f6e" > 366< / a > < / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#aaef7fc778a29bb3bb3040c0423937f6e" > EntryCoefficient< / a > (EntryIndex i)< span class = "keyword" > const < / span > { < span class = "keywordflow" > return< / span > coefficients_[i]; }< / div >
< div class = "line" > < a id = "l00367" name = "l00367" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#aedc46de5199e203b77de2eae2e4c100d" > 367< / a > < / span > RowIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#aedc46de5199e203b77de2eae2e4c100d" > EntryRow< / a > (EntryIndex i)< span class = "keyword" > const < / span > { < span class = "keywordflow" > return< / span > rows_[i]; }< / div >
< div class = "line" > < a id = "l00368" name = "l00368" > < / a > < span class = "lineno" > 368< / span > < / div >
< div class = "line" > < a id = "l00369" name = "l00369" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#afbe7c81d6b4066bf7874299a0f7c0d59" > 369< / a > < / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_column_view.html" > ColumnView< / a > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#afbe7c81d6b4066bf7874299a0f7c0d59" > column< / a > (ColIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > )< span class = "keyword" > const < / span > {< / div >
< div class = "line" > < a id = "l00370" name = "l00370" > < / a > < span class = "lineno" > 370< / span > < a class = "code hl_define" href = "base_2logging_8h.html#ab62f5ed8f2d48e29802be0cbbcd1359a" > DCHECK_LT< / a > (< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > , num_cols_);< / div >
< div class = "line" > < a id = "l00371" name = "l00371" > < / a > < span class = "lineno" > 371< / span > < / div >
< div class = "line" > < a id = "l00372" name = "l00372" > < / a > < span class = "lineno" > 372< / span > < span class = "comment" > // Note that the start may be equal to row.size() if the last columns< / span > < / div >
< div class = "line" > < a id = "l00373" name = "l00373" > < / a > < span class = "lineno" > 373< / span > < span class = "comment" > // are empty, it is why we don' t use & row[start].< / span > < / div >
< div class = "line" > < a id = "l00374" name = "l00374" > < / a > < span class = "lineno" > 374< / span > < span class = "keyword" > const< / span > EntryIndex start = starts_[< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > ];< / div >
< div class = "line" > < a id = "l00375" name = "l00375" > < / a > < span class = "lineno" > 375< / span > < span class = "keywordflow" > return< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_column_view.html" > ColumnView< / a > (starts_[< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > + 1] - start, rows_.data() + start.value(),< / div >
< div class = "line" > < a id = "l00376" name = "l00376" > < / a > < span class = "lineno" > 376< / span > coefficients_.data() + start.value());< / div >
< div class = "line" > < a id = "l00377" name = "l00377" > < / a > < span class = "lineno" > 377< / span > }< / div >
< div class = "line" > < a id = "l00378" name = "l00378" > < / a > < span class = "lineno" > 378< / span > < / div >
< div class = "line" > < a id = "l00379" name = "l00379" > < / a > < span class = "lineno" > 379< / span > < span class = "comment" > // Returns true if the given column is empty. Note that for triangular matrix< / span > < / div >
< div class = "line" > < a id = "l00380" name = "l00380" > < / a > < span class = "lineno" > 380< / span > < span class = "comment" > // this does not include the diagonal coefficient (see below).< / span > < / div >
< div class = "line" > < a id = "l00381" name = "l00381" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a1426d8ab983ec32193c571f5e8c02cda" > 381< / a > < / span > < span class = "keywordtype" > bool< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a1426d8ab983ec32193c571f5e8c02cda" > ColumnIsEmpty< / a > (ColIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > )< span class = "keyword" > const < / span > {< / div >
< div class = "line" > < a id = "l00382" name = "l00382" > < / a > < span class = "lineno" > 382< / span > < span class = "keywordflow" > return< / span > starts_[< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > + 1] == starts_[< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > ];< / div >
< div class = "line" > < a id = "l00383" name = "l00383" > < / a > < span class = "lineno" > 383< / span > }< / div >
< div class = "line" > < a id = "l00384" name = "l00384" > < / a > < span class = "lineno" > 384< / span > < / div >
< div class = "line" > < a id = "l00385" name = "l00385" > < / a > < span class = "lineno" > 385< / span > < span class = "comment" > // Returns the scalar product of the given row vector with the column of index< / span > < / div >
< div class = "line" > < a id = "l00386" name = "l00386" > < / a > < span class = "lineno" > 386< / span > < span class = "comment" > // col of this matrix. This function is declared in the .h for efficiency.< / span > < / div >
< div class = "line" > < a id = "l00387" name = "l00387" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#abdd940ad64b555052b33e763b80aea26" > 387< / a > < / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#abdd940ad64b555052b33e763b80aea26" > ColumnScalarProduct< / a > (ColIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > , < span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseRow< / a > & vector)< span class = "keyword" > const < / span > {< / div >
< div class = "line" > < a id = "l00388" name = "l00388" > < / a > < span class = "lineno" > 388< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > result = 0.0;< / div >
< div class = "line" > < a id = "l00389" name = "l00389" > < / a > < span class = "lineno" > 389< / span > < span class = "keywordflow" > for< / span > (< span class = "keyword" > const< / span > EntryIndex i : Column(< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > )) {< / div >
< div class = "line" > < a id = "l00390" name = "l00390" > < / a > < span class = "lineno" > 390< / span > result += EntryCoefficient(i) * vector[< a class = "code hl_function" href = "namespaceoperations__research_1_1glop.html#a8fbc9efd86a3cc862a9079d86ab8b524" > RowToColIndex< / a > (EntryRow(i))];< / div >
< div class = "line" > < a id = "l00391" name = "l00391" > < / a > < span class = "lineno" > 391< / span > }< / div >
< div class = "line" > < a id = "l00392" name = "l00392" > < / a > < span class = "lineno" > 392< / span > < span class = "keywordflow" > return< / span > result;< / div >
< div class = "line" > < a id = "l00393" name = "l00393" > < / a > < span class = "lineno" > 393< / span > }< / div >
< div class = "line" > < a id = "l00394" name = "l00394" > < / a > < span class = "lineno" > 394< / span > < / div >
< div class = "line" > < a id = "l00395" name = "l00395" > < / a > < span class = "lineno" > 395< / span > < span class = "comment" > // Adds a multiple of the given column of this matrix to the given< / span > < / div >
< div class = "line" > < a id = "l00396" name = "l00396" > < / a > < span class = "lineno" > 396< / span > < span class = "comment" > // dense_column. If multiplier is 0.0, this function does nothing. This< / span > < / div >
< div class = "line" > < a id = "l00397" name = "l00397" > < / a > < span class = "lineno" > 397< / span > < span class = "comment" > // function is declared in the .h for efficiency.< / span > < / div >
< div class = "line" > < a id = "l00398" name = "l00398" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#aea0e9a84b41c95c874f171cae97cf31b" > 398< / a > < / span > < span class = "keywordtype" > void< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#aea0e9a84b41c95c874f171cae97cf31b" > ColumnAddMultipleToDenseColumn< / a > (ColIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > , < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > multiplier,< / div >
< div class = "line" > < a id = "l00399" name = "l00399" > < / a > < span class = "lineno" > 399< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > * dense_column)< span class = "keyword" > const < / span > {< / div >
< div class = "line" > < a id = "l00400" name = "l00400" > < / a > < span class = "lineno" > 400< / span > < span class = "keywordflow" > if< / span > (multiplier == 0.0) < span class = "keywordflow" > return< / span > ;< / div >
< div class = "line" > < a id = "l00401" name = "l00401" > < / a > < span class = "lineno" > 401< / span > < a class = "code hl_define" href = "return__macros_8h.html#a6009315499028d98072d8f31834cf4f9" > RETURN_IF_NULL< / a > (dense_column);< / div >
< div class = "line" > < a id = "l00402" name = "l00402" > < / a > < span class = "lineno" > 402< / span > < span class = "keywordflow" > for< / span > (< span class = "keyword" > const< / span > EntryIndex i : Column(< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > )) {< / div >
< div class = "line" > < a id = "l00403" name = "l00403" > < / a > < span class = "lineno" > 403< / span > (*dense_column)[EntryRow(i)] += multiplier * EntryCoefficient(i);< / div >
< div class = "line" > < a id = "l00404" name = "l00404" > < / a > < span class = "lineno" > 404< / span > }< / div >
< div class = "line" > < a id = "l00405" name = "l00405" > < / a > < span class = "lineno" > 405< / span > }< / div >
< div class = "line" > < a id = "l00406" name = "l00406" > < / a > < span class = "lineno" > 406< / span > < / div >
< div class = "line" > < a id = "l00407" name = "l00407" > < / a > < span class = "lineno" > 407< / span > < span class = "comment" > // Same as ColumnAddMultipleToDenseColumn() but also adds the new non-zeros to< / span > < / div >
< div class = "line" > < a id = "l00408" name = "l00408" > < / a > < span class = "lineno" > 408< / span > < span class = "comment" > // the non_zeros vector. A non-zero is " new" if is_non_zero[row] was false,< / span > < / div >
< div class = "line" > < a id = "l00409" name = "l00409" > < / a > < span class = "lineno" > 409< / span > < span class = "comment" > // and we update dense_column[row]. This function also updates is_non_zero.< / span > < / div >
< div class = "line" > < a id = "l00410" name = "l00410" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a6e49e4127a33039fcccc6e50380faefa" > 410< / a > < / span > < span class = "keywordtype" > void< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a6e49e4127a33039fcccc6e50380faefa" > ColumnAddMultipleToSparseScatteredColumn< / a > (ColIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > ,< / div >
< div class = "line" > < a id = "l00411" name = "l00411" > < / a > < span class = "lineno" > 411< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > multiplier,< / div >
< div class = "line" > < a id = "l00412" name = "l00412" > < / a > < span class = "lineno" > 412< / span > < a class = "code hl_struct" href = "structoperations__research_1_1glop_1_1_scattered_column.html" > ScatteredColumn< / a > * column)< span class = "keyword" > const < / span > {< / div >
< div class = "line" > < a id = "l00413" name = "l00413" > < / a > < span class = "lineno" > 413< / span > < span class = "keywordflow" > if< / span > (multiplier == 0.0) < span class = "keywordflow" > return< / span > ;< / div >
< div class = "line" > < a id = "l00414" name = "l00414" > < / a > < span class = "lineno" > 414< / span > < a class = "code hl_define" href = "return__macros_8h.html#a6009315499028d98072d8f31834cf4f9" > RETURN_IF_NULL< / a > (column);< / div >
< div class = "line" > < a id = "l00415" name = "l00415" > < / a > < span class = "lineno" > 415< / span > < span class = "keywordflow" > for< / span > (< span class = "keyword" > const< / span > EntryIndex i : Column(< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > )) {< / div >
< div class = "line" > < a id = "l00416" name = "l00416" > < / a > < span class = "lineno" > 416< / span > < span class = "keyword" > const< / span > RowIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aea35f36ba98d5bbd8d033382f50c9e52" > row< / a > = EntryRow(i);< / div >
< div class = "line" > < a id = "l00417" name = "l00417" > < / a > < span class = "lineno" > 417< / span > column-> < a class = "code hl_function" href = "structoperations__research_1_1glop_1_1_scattered_vector.html#a9bb4f0967311f0f79a279879c4d69678" > Add< / a > (< a class = "code hl_variable" href = "markowitz_8cc.html#aea35f36ba98d5bbd8d033382f50c9e52" > row< / a > , multiplier * EntryCoefficient(i));< / div >
< div class = "line" > < a id = "l00418" name = "l00418" > < / a > < span class = "lineno" > 418< / span > }< / div >
< div class = "line" > < a id = "l00419" name = "l00419" > < / a > < span class = "lineno" > 419< / span > }< / div >
< div class = "line" > < a id = "l00420" name = "l00420" > < / a > < span class = "lineno" > 420< / span > < / div >
< div class = "line" > < a id = "l00421" name = "l00421" > < / a > < span class = "lineno" > 421< / span > < span class = "comment" > // Copies the given column of this matrix into the given dense_column.< / span > < / div >
< div class = "line" > < a id = "l00422" name = "l00422" > < / a > < span class = "lineno" > 422< / span > < span class = "comment" > // This function is declared in the .h for efficiency.< / span > < / div >
< div class = "line" > < a id = "l00423" name = "l00423" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a28058c5e9ff6638ea1ea210b49a4e7bc" > 423< / a > < / span > < span class = "keywordtype" > void< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a28058c5e9ff6638ea1ea210b49a4e7bc" > ColumnCopyToDenseColumn< / a > (ColIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > , < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > * dense_column)< span class = "keyword" > const < / span > {< / div >
< div class = "line" > < a id = "l00424" name = "l00424" > < / a > < span class = "lineno" > 424< / span > < a class = "code hl_define" href = "return__macros_8h.html#a6009315499028d98072d8f31834cf4f9" > RETURN_IF_NULL< / a > (dense_column);< / div >
< div class = "line" > < a id = "l00425" name = "l00425" > < / a > < span class = "lineno" > 425< / span > dense_column-> < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html#a3de922485ca2c30f3e07d959dd97cdd0" > AssignToZero< / a > (num_rows_);< / div >
< div class = "line" > < a id = "l00426" name = "l00426" > < / a > < span class = "lineno" > 426< / span > ColumnCopyToClearedDenseColumn(< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > , dense_column);< / div >
< div class = "line" > < a id = "l00427" name = "l00427" > < / a > < span class = "lineno" > 427< / span > }< / div >
< div class = "line" > < a id = "l00428" name = "l00428" > < / a > < span class = "lineno" > 428< / span > < / div >
< div class = "line" > < a id = "l00429" name = "l00429" > < / a > < span class = "lineno" > 429< / span > < span class = "comment" > // Same as ColumnCopyToDenseColumn() but assumes the column to be initially< / span > < / div >
< div class = "line" > < a id = "l00430" name = "l00430" > < / a > < span class = "lineno" > 430< / span > < span class = "comment" > // all zero.< / span > < / div >
< div class = "line" > < a id = "l00431" name = "l00431" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#ab9bd1cef3f6a18704cb7d9ce6201e106" > 431< / a > < / span > < span class = "keywordtype" > void< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#ab9bd1cef3f6a18704cb7d9ce6201e106" > ColumnCopyToClearedDenseColumn< / a > (ColIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > ,< / div >
< div class = "line" > < a id = "l00432" name = "l00432" > < / a > < span class = "lineno" > 432< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > * dense_column)< span class = "keyword" > const < / span > {< / div >
< div class = "line" > < a id = "l00433" name = "l00433" > < / a > < span class = "lineno" > 433< / span > < a class = "code hl_define" href = "return__macros_8h.html#a6009315499028d98072d8f31834cf4f9" > RETURN_IF_NULL< / a > (dense_column);< / div >
< div class = "line" > < a id = "l00434" name = "l00434" > < / a > < span class = "lineno" > 434< / span > dense_column-> < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html#a64b6b04f3a519d2c61d49daaa88bf06e" > resize< / a > (num_rows_, 0.0);< / div >
< div class = "line" > < a id = "l00435" name = "l00435" > < / a > < span class = "lineno" > 435< / span > < span class = "keywordflow" > for< / span > (< span class = "keyword" > const< / span > EntryIndex i : Column(< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > )) {< / div >
< div class = "line" > < a id = "l00436" name = "l00436" > < / a > < span class = "lineno" > 436< / span > (*dense_column)[EntryRow(i)] = EntryCoefficient(i);< / div >
< div class = "line" > < a id = "l00437" name = "l00437" > < / a > < span class = "lineno" > 437< / span > }< / div >
< div class = "line" > < a id = "l00438" name = "l00438" > < / a > < span class = "lineno" > 438< / span > }< / div >
< div class = "line" > < a id = "l00439" name = "l00439" > < / a > < span class = "lineno" > 439< / span > < / div >
< div class = "line" > < a id = "l00440" name = "l00440" > < / a > < span class = "lineno" > 440< / span > < span class = "comment" > // Same as ColumnCopyToClearedDenseColumn() but also fills non_zeros.< / span > < / div >
< div class = "line" > < a id = "l00441" name = "l00441" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a8a0e8a1a3afc70e2678d046feb11d024" > 441< / a > < / span > < span class = "keywordtype" > void< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a8a0e8a1a3afc70e2678d046feb11d024" > ColumnCopyToClearedDenseColumnWithNonZeros< / a > (< / div >
< div class = "line" > < a id = "l00442" name = "l00442" > < / a > < span class = "lineno" > 442< / span > ColIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > , < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > * dense_column,< / div >
< div class = "line" > < a id = "l00443" name = "l00443" > < / a > < span class = "lineno" > 443< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#ac014de658aabf122011e8fb07b6f4612" > RowIndexVector< / a > * non_zeros)< span class = "keyword" > const < / span > {< / div >
< div class = "line" > < a id = "l00444" name = "l00444" > < / a > < span class = "lineno" > 444< / span > < a class = "code hl_define" href = "return__macros_8h.html#a6009315499028d98072d8f31834cf4f9" > RETURN_IF_NULL< / a > (dense_column);< / div >
< div class = "line" > < a id = "l00445" name = "l00445" > < / a > < span class = "lineno" > 445< / span > dense_column-> < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html#a64b6b04f3a519d2c61d49daaa88bf06e" > resize< / a > (num_rows_, 0.0);< / div >
< div class = "line" > < a id = "l00446" name = "l00446" > < / a > < span class = "lineno" > 446< / span > non_zeros-> clear();< / div >
< div class = "line" > < a id = "l00447" name = "l00447" > < / a > < span class = "lineno" > 447< / span > < span class = "keywordflow" > for< / span > (< span class = "keyword" > const< / span > EntryIndex i : Column(< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > )) {< / div >
< div class = "line" > < a id = "l00448" name = "l00448" > < / a > < span class = "lineno" > 448< / span > < span class = "keyword" > const< / span > RowIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aea35f36ba98d5bbd8d033382f50c9e52" > row< / a > = EntryRow(i);< / div >
< div class = "line" > < a id = "l00449" name = "l00449" > < / a > < span class = "lineno" > 449< / span > (*dense_column)[< a class = "code hl_variable" href = "markowitz_8cc.html#aea35f36ba98d5bbd8d033382f50c9e52" > row< / a > ] = EntryCoefficient(i);< / div >
< div class = "line" > < a id = "l00450" name = "l00450" > < / a > < span class = "lineno" > 450< / span > non_zeros-> push_back(< a class = "code hl_variable" href = "markowitz_8cc.html#aea35f36ba98d5bbd8d033382f50c9e52" > row< / a > );< / div >
< div class = "line" > < a id = "l00451" name = "l00451" > < / a > < span class = "lineno" > 451< / span > }< / div >
< div class = "line" > < a id = "l00452" name = "l00452" > < / a > < span class = "lineno" > 452< / span > }< / div >
< div class = "line" > < a id = "l00453" name = "l00453" > < / a > < span class = "lineno" > 453< / span > < / div >
< div class = "line" > < a id = "l00454" name = "l00454" > < / a > < span class = "lineno" > 454< / span > < span class = "keywordtype" > void< / span > Swap(< a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html" > CompactSparseMatrix< / a > * other);< / div >
< div class = "line" > < a id = "l00455" name = "l00455" > < / a > < span class = "lineno" > 455< / span > < / div >
< div class = "line" > < a id = "l00456" name = "l00456" > < / a > < span class = "lineno" > 456< / span > < span class = "keyword" > protected< / span > :< / div >
< div class = "line" > < a id = "l00457" name = "l00457" > < / a > < span class = "lineno" > 457< / span > < span class = "comment" > // The matrix dimensions, properly updated by full and incremental builders.< / span > < / div >
< div class = "line" > < a id = "l00458" name = "l00458" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a5d6d5d7a7944b09bd0df4b7132fe5f7e" > 458< / a > < / span > RowIndex < a class = "code hl_variable" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a5d6d5d7a7944b09bd0df4b7132fe5f7e" > num_rows_< / a > ;< / div >
< div class = "line" > < a id = "l00459" name = "l00459" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a0358eb2d6ea480b59d89dc42326cf840" > 459< / a > < / span > ColIndex < a class = "code hl_variable" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a0358eb2d6ea480b59d89dc42326cf840" > num_cols_< / a > ;< / div >
< div class = "line" > < a id = "l00460" name = "l00460" > < / a > < span class = "lineno" > 460< / span > < / div >
< div class = "line" > < a id = "l00461" name = "l00461" > < / a > < span class = "lineno" > 461< / span > < span class = "comment" > // Holds the columns non-zero coefficients and row positions.< / span > < / div >
< div class = "line" > < a id = "l00462" name = "l00462" > < / a > < span class = "lineno" > 462< / span > < span class = "comment" > // The entries for the column of index col are stored in the entries< / span > < / div >
< div class = "line" > < a id = "l00463" name = "l00463" > < / a > < span class = "lineno" > 463< / span > < span class = "comment" > // [starts_[col], starts_[col + 1]).< / span > < / div >
< div class = "line" > < a id = "l00464" name = "l00464" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#ab392807d136adb480aedec7750cbbb18" > 464< / a > < / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > StrictITIVector< EntryIndex, Fractional> < / a > < a class = "code hl_variable" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#ab392807d136adb480aedec7750cbbb18" > coefficients_< / a > ;< / div >
< div class = "line" > < a id = "l00465" name = "l00465" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a8da36920b149053499a21e50fc859a93" > 465< / a > < / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > StrictITIVector< EntryIndex, RowIndex> < / a > < a class = "code hl_variable" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a8da36920b149053499a21e50fc859a93" > rows_< / a > ;< / div >
< div class = "line" > < a id = "l00466" name = "l00466" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a37ac057f213297550a26947d551324a3" > 466< / a > < / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > StrictITIVector< ColIndex, EntryIndex> < / a > < a class = "code hl_variable" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a37ac057f213297550a26947d551324a3" > starts_< / a > ;< / div >
< div class = "line" > < a id = "l00467" name = "l00467" > < / a > < span class = "lineno" > 467< / span > < / div >
< div class = "line" > < a id = "l00468" name = "l00468" > < / a > < span class = "lineno" > 468< / span > < span class = "keyword" > private< / span > :< / div >
< div class = "line" > < a id = "l00469" name = "l00469" > < / a > < span class = "lineno" > 469< / span > < a class = "code hl_define" href = "macros_8h.html#af8df3547bfde53a5acb93e2607b0034a" > DISALLOW_COPY_AND_ASSIGN< / a > (< a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html" > CompactSparseMatrix< / a > );< / div >
< div class = "line" > < a id = "l00470" name = "l00470" > < / a > < span class = "lineno" > 470< / span > };< / div >
< div class = "line" > < a id = "l00471" name = "l00471" > < / a > < span class = "lineno" > 471< / span > < / div >
< div class = "line" > < a id = "l00472" name = "l00472" > < / a > < span class = "lineno" > 472< / span > < span class = "comment" > // A matrix view of the basis columns of a CompactSparseMatrix, with basis< / span > < / div >
< div class = "line" > < a id = "l00473" name = "l00473" > < / a > < span class = "lineno" > 473< / span > < span class = "comment" > // specified as a RowToColMapping. This class does not take ownership of the< / span > < / div >
< div class = "line" > < a id = "l00474" name = "l00474" > < / a > < span class = "lineno" > 474< / span > < span class = "comment" > // underlying matrix or basis, and thus they must outlive this class (and keep< / span > < / div >
< div class = "line" > < a id = "l00475" name = "l00475" > < / a > < span class = "lineno" > 475< / span > < span class = "comment" > // the same address in memory).< / span > < / div >
< div class = "line" > < a id = "l00476" name = "l00476" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix_view.html" > 476< / a > < / span > < span class = "keyword" > class < / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix_view.html" > CompactSparseMatrixView< / a > {< / div >
< div class = "line" > < a id = "l00477" name = "l00477" > < / a > < span class = "lineno" > 477< / span > < span class = "keyword" > public< / span > :< / div >
< div class = "line" > < a id = "l00478" name = "l00478" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix_view.html#a6e483ed6906f126dc6fa63d198c8f907" > 478< / a > < / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix_view.html#a6e483ed6906f126dc6fa63d198c8f907" > CompactSparseMatrixView< / a > (< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html" > CompactSparseMatrix< / a > * compact_matrix,< / div >
< div class = "line" > < a id = "l00479" name = "l00479" > < / a > < span class = "lineno" > 479< / span > < span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > RowToColMapping< / a > * basis)< / div >
< div class = "line" > < a id = "l00480" name = "l00480" > < / a > < span class = "lineno" > 480< / span > : compact_matrix_(*compact_matrix),< / div >
< div class = "line" > < a id = "l00481" name = "l00481" > < / a > < span class = "lineno" > 481< / span > columns_(basis-> data(), basis-> size().< a class = "code hl_variable" href = "demon__profiler_8cc.html#ac072af30c4ffbc834bb4c681f6ecb514" > value< / a > ()) {}< / div >
< div class = "line" > < a id = "l00482" name = "l00482" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix_view.html#a7a56df5528de85e8d1b588d6a50e6948" > 482< / a > < / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix_view.html#a7a56df5528de85e8d1b588d6a50e6948" > CompactSparseMatrixView< / a > (< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html" > CompactSparseMatrix< / a > * compact_matrix,< / div >
< div class = "line" > < a id = "l00483" name = "l00483" > < / a > < span class = "lineno" > 483< / span > < span class = "keyword" > const< / span > std::vector< ColIndex> * columns)< / div >
< div class = "line" > < a id = "l00484" name = "l00484" > < / a > < span class = "lineno" > 484< / span > : compact_matrix_(*compact_matrix), columns_(*columns) {}< / div >
< div class = "line" > < a id = "l00485" name = "l00485" > < / a > < span class = "lineno" > 485< / span > < / div >
< div class = "line" > < a id = "l00486" name = "l00486" > < / a > < span class = "lineno" > 486< / span > < span class = "comment" > // Same behavior as the SparseMatrix functions above.< / span > < / div >
< div class = "line" > < a id = "l00487" name = "l00487" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix_view.html#a8e12342fc420701fbffd97025421575a" > 487< / a > < / span > < span class = "keywordtype" > bool< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix_view.html#a8e12342fc420701fbffd97025421575a" > IsEmpty< / a > ()< span class = "keyword" > const < / span > { < span class = "keywordflow" > return< / span > compact_matrix_.IsEmpty(); }< / div >
< div class = "line" > < a id = "l00488" name = "l00488" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix_view.html#a960110e64357a3e69162ebf1f71959dd" > 488< / a > < / span > RowIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix_view.html#a960110e64357a3e69162ebf1f71959dd" > num_rows< / a > ()< span class = "keyword" > const < / span > { < span class = "keywordflow" > return< / span > compact_matrix_.num_rows(); }< / div >
< div class = "line" > < a id = "l00489" name = "l00489" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix_view.html#a41741829541d089f1c4d34f190884813" > 489< / a > < / span > ColIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix_view.html#a41741829541d089f1c4d34f190884813" > num_cols< / a > ()< span class = "keyword" > const < / span > { < span class = "keywordflow" > return< / span > ColIndex(columns_.size()); }< / div >
< div class = "line" > < a id = "l00490" name = "l00490" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix_view.html#a6c2cb025b83ee5d5365eb0b419a0298c" > 490< / a > < / span > < span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_column_view.html" > ColumnView< / a > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix_view.html#a6c2cb025b83ee5d5365eb0b419a0298c" > column< / a > (ColIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > )< span class = "keyword" > const < / span > {< / div >
< div class = "line" > < a id = "l00491" name = "l00491" > < / a > < span class = "lineno" > 491< / span > < span class = "keywordflow" > return< / span > compact_matrix_.column(columns_[< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > .value()]);< / div >
< div class = "line" > < a id = "l00492" name = "l00492" > < / a > < span class = "lineno" > 492< / span > }< / div >
< div class = "line" > < a id = "l00493" name = "l00493" > < / a > < span class = "lineno" > 493< / span > EntryIndex < a class = "code hl_variable" href = "preprocessor_8cc.html#a2babe18010525bbf13c2fa5a959971e4" > num_entries< / a > () < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00494" name = "l00494" > < / a > < span class = "lineno" > 494< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > ComputeOneNorm() < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00495" name = "l00495" > < / a > < span class = "lineno" > 495< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > < a class = "code hl_function" href = "namespaceoperations__research_1_1sat.html#acb294633c7688f918623b3b0e09aec43" > ComputeInfinityNorm< / a > () < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00496" name = "l00496" > < / a > < span class = "lineno" > 496< / span > < / div >
< div class = "line" > < a id = "l00497" name = "l00497" > < / a > < span class = "lineno" > 497< / span > < span class = "keyword" > private< / span > :< / div >
< div class = "line" > < a id = "l00498" name = "l00498" > < / a > < span class = "lineno" > 498< / span > < span class = "comment" > // We require that the underlying CompactSparseMatrix and RowToColMapping< / span > < / div >
< div class = "line" > < a id = "l00499" name = "l00499" > < / a > < span class = "lineno" > 499< / span > < span class = "comment" > // continue to own the (potentially large) data accessed via this view.< / span > < / div >
< div class = "line" > < a id = "l00500" name = "l00500" > < / a > < span class = "lineno" > 500< / span > < span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html" > CompactSparseMatrix< / a > & compact_matrix_;< / div >
< div class = "line" > < a id = "l00501" name = "l00501" > < / a > < span class = "lineno" > 501< / span > < span class = "keyword" > const< / span > absl::Span< const ColIndex> columns_;< / div >
< div class = "line" > < a id = "l00502" name = "l00502" > < / a > < span class = "lineno" > 502< / span > };< / div >
< div class = "line" > < a id = "l00503" name = "l00503" > < / a > < span class = "lineno" > 503< / span > < / div >
< div class = "line" > < a id = "l00504" name = "l00504" > < / a > < span class = "lineno" > 504< / span > < span class = "comment" > // Specialization of a CompactSparseMatrix used for triangular matrices.< / span > < / div >
< div class = "line" > < a id = "l00505" name = "l00505" > < / a > < span class = "lineno" > 505< / span > < span class = "comment" > // To be able to solve triangular systems as efficiently as possible, the< / span > < / div >
< div class = "line" > < a id = "l00506" name = "l00506" > < / a > < span class = "lineno" > 506< / span > < span class = "comment" > // diagonal entries are stored in a separate vector and not in the underlying< / span > < / div >
< div class = "line" > < a id = "l00507" name = "l00507" > < / a > < span class = "lineno" > 507< / span > < span class = "comment" > // CompactSparseMatrix.< / span > < / div >
< div class = "line" > < a id = "l00508" name = "l00508" > < / a > < span class = "lineno" > 508< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00509" name = "l00509" > < / a > < span class = "lineno" > 509< / span > < span class = "comment" > // Advanced usage: this class also support matrices that can be permuted into a< / span > < / div >
< div class = "line" > < a id = "l00510" name = "l00510" > < / a > < span class = "lineno" > 510< / span > < span class = "comment" > // triangular matrix and some functions work directly on such matrices.< / span > < / div >
< div class = "line" > < a id = "l00511" name = "l00511" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_triangular_matrix.html" > 511< / a > < / span > < span class = "keyword" > class < / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_triangular_matrix.html" > TriangularMatrix< / a > : < span class = "keyword" > private< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html" > CompactSparseMatrix< / a > {< / div >
< div class = "line" > < a id = "l00512" name = "l00512" > < / a > < span class = "lineno" > 512< / span > < span class = "keyword" > public< / span > :< / div >
< div class = "line" > < a id = "l00513" name = "l00513" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#ab8101094fb842f9cb500b3dfadc325d3" > 513< / a > < / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#ab8101094fb842f9cb500b3dfadc325d3" > TriangularMatrix< / a > () : all_diagonal_coefficients_are_one_(true) {}< / div >
< div class = "line" > < a id = "l00514" name = "l00514" > < / a > < span class = "lineno" > 514< / span > < / div >
< div class = "line" > < a id = "l00515" name = "l00515" > < / a > < span class = "lineno" > 515< / span > < span class = "comment" > // Only a subset of the functions from CompactSparseMatrix are exposed (note< / span > < / div >
< div class = "line" > < a id = "l00516" name = "l00516" > < / a > < span class = "lineno" > 516< / span > < span class = "comment" > // the private inheritance). They are extended to deal with diagonal< / span > < / div >
< div class = "line" > < a id = "l00517" name = "l00517" > < / a > < span class = "lineno" > 517< / span > < span class = "comment" > // coefficients properly.< / span > < / div >
< div class = "line" > < a id = "l00518" name = "l00518" > < / a > < span class = "lineno" > 518< / span > < span class = "keywordtype" > void< / span > PopulateFromTranspose(< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_triangular_matrix.html" > TriangularMatrix< / a > & < a class = "code hl_function" href = "parser_8yy_8cc.html#a5a634cf4429798b1c921a81de8250051" > input< / a > );< / div >
< div class = "line" > < a id = "l00519" name = "l00519" > < / a > < span class = "lineno" > 519< / span > < span class = "keywordtype" > void< / span > Swap(< a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_triangular_matrix.html" > TriangularMatrix< / a > * other);< / div >
< div class = "line" > < a id = "l00520" name = "l00520" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#a8e12342fc420701fbffd97025421575a" > 520< / a > < / span > < span class = "keywordtype" > bool< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#a8e12342fc420701fbffd97025421575a" > IsEmpty< / a > ()< span class = "keyword" > const < / span > { < span class = "keywordflow" > return< / span > diagonal_coefficients_.empty(); }< / div >
< div class = "line" > < a id = "l00521" name = "l00521" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#a960110e64357a3e69162ebf1f71959dd" > 521< / a > < / span > RowIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#a960110e64357a3e69162ebf1f71959dd" > num_rows< / a > ()< span class = "keyword" > const < / span > { < span class = "keywordflow" > return< / span > num_rows_; }< / div >
< div class = "line" > < a id = "l00522" name = "l00522" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#a41741829541d089f1c4d34f190884813" > 522< / a > < / span > ColIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#a41741829541d089f1c4d34f190884813" > num_cols< / a > ()< span class = "keyword" > const < / span > { < span class = "keywordflow" > return< / span > num_cols_; }< / div >
< div class = "line" > < a id = "l00523" name = "l00523" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#af69d9b7065a8f31604a8134be4307749" > 523< / a > < / span > EntryIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#af69d9b7065a8f31604a8134be4307749" > num_entries< / a > ()< span class = "keyword" > const < / span > {< / div >
< div class = "line" > < a id = "l00524" name = "l00524" > < / a > < span class = "lineno" > 524< / span > < span class = "keywordflow" > return< / span > EntryIndex(num_cols_.value()) + coefficients_.size();< / div >
< div class = "line" > < a id = "l00525" name = "l00525" > < / a > < span class = "lineno" > 525< / span > }< / div >
< div class = "line" > < a id = "l00526" name = "l00526" > < / a > < span class = "lineno" > 526< / span > < / div >
< div class = "line" > < a id = "l00527" name = "l00527" > < / a > < span class = "lineno" > 527< / span > < span class = "comment" > // On top of the CompactSparseMatrix functionality, TriangularMatrix::Reset()< / span > < / div >
< div class = "line" > < a id = "l00528" name = "l00528" > < / a > < span class = "lineno" > 528< / span > < span class = "comment" > // also pre-allocates space of size col_size for a number of internal vectors.< / span > < / div >
< div class = "line" > < a id = "l00529" name = "l00529" > < / a > < span class = "lineno" > 529< / span > < span class = "comment" > // This helps reduce costly push_back operations for large problems.< / span > < / div >
< div class = "line" > < a id = "l00530" name = "l00530" > < / a > < span class = "lineno" > 530< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00531" name = "l00531" > < / a > < span class = "lineno" > 531< / span > < span class = "comment" > // WARNING: Reset() must be called with a sufficiently large col_capacity< / span > < / div >
< div class = "line" > < a id = "l00532" name = "l00532" > < / a > < span class = "lineno" > 532< / span > < span class = "comment" > // prior to any Add* calls (e.g., AddTriangularColumn).< / span > < / div >
< div class = "line" > < a id = "l00533" name = "l00533" > < / a > < span class = "lineno" > 533< / span > < span class = "keywordtype" > void< / span > Reset(RowIndex num_rows, ColIndex col_capacity);< / div >
< div class = "line" > < a id = "l00534" name = "l00534" > < / a > < span class = "lineno" > 534< / span > < / div >
< div class = "line" > < a id = "l00535" name = "l00535" > < / a > < span class = "lineno" > 535< / span > < span class = "comment" > // Constructs a triangular matrix from the given SparseMatrix. The input is< / span > < / div >
< div class = "line" > < a id = "l00536" name = "l00536" > < / a > < span class = "lineno" > 536< / span > < span class = "comment" > // assumed to be lower or upper triangular without any permutations. This is< / span > < / div >
< div class = "line" > < a id = "l00537" name = "l00537" > < / a > < span class = "lineno" > 537< / span > < span class = "comment" > // checked in debug mode.< / span > < / div >
< div class = "line" > < a id = "l00538" name = "l00538" > < / a > < span class = "lineno" > 538< / span > < span class = "keywordtype" > void< / span > PopulateFromTriangularSparseMatrix(< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html" > SparseMatrix< / a > & < a class = "code hl_function" href = "parser_8yy_8cc.html#a5a634cf4429798b1c921a81de8250051" > input< / a > );< / div >
< div class = "line" > < a id = "l00539" name = "l00539" > < / a > < span class = "lineno" > 539< / span > < / div >
< div class = "line" > < a id = "l00540" name = "l00540" > < / a > < span class = "lineno" > 540< / span > < span class = "comment" > // Functions to create a triangular matrix incrementally, column by column.< / span > < / div >
< div class = "line" > < a id = "l00541" name = "l00541" > < / a > < span class = "lineno" > 541< / span > < span class = "comment" > // A client needs to call Reset(num_rows) first, and then each column must be< / span > < / div >
< div class = "line" > < a id = "l00542" name = "l00542" > < / a > < span class = "lineno" > 542< / span > < span class = "comment" > // added by calling one of the 3 functions below.< / span > < / div >
< div class = "line" > < a id = "l00543" name = "l00543" > < / a > < span class = "lineno" > 543< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00544" name = "l00544" > < / a > < span class = "lineno" > 544< / span > < span class = "comment" > // Note that the row indices of the columns are allowed to be permuted: the< / span > < / div >
< div class = "line" > < a id = "l00545" name = "l00545" > < / a > < span class = "lineno" > 545< / span > < span class = "comment" > // diagonal entry of the column #col not being necessarily on the row #col.< / span > < / div >
< div class = "line" > < a id = "l00546" name = "l00546" > < / a > < span class = "lineno" > 546< / span > < span class = "comment" > // This is why these functions require the ' diagonal_row' parameter. The< / span > < / div >
< div class = "line" > < a id = "l00547" name = "l00547" > < / a > < span class = "lineno" > 547< / span > < span class = "comment" > // permutation can be fixed at the end by a call to< / span > < / div >
< div class = "line" > < a id = "l00548" name = "l00548" > < / a > < span class = "lineno" > 548< / span > < span class = "comment" > // ApplyRowPermutationToNonDiagonalEntries() or accounted directly in the case< / span > < / div >
< div class = "line" > < a id = "l00549" name = "l00549" > < / a > < span class = "lineno" > 549< / span > < span class = "comment" > // of PermutedLowerSparseSolve().< / span > < / div >
< div class = "line" > < a id = "l00550" name = "l00550" > < / a > < span class = "lineno" > 550< / span > < span class = "keywordtype" > void< / span > AddTriangularColumn(< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_column_view.html" > ColumnView< / a > & column, RowIndex diagonal_row);< / div >
< div class = "line" > < a id = "l00551" name = "l00551" > < / a > < span class = "lineno" > 551< / span > < span class = "keywordtype" > void< / span > AddTriangularColumnWithGivenDiagonalEntry(< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_column.html" > SparseColumn< / a > & column,< / div >
< div class = "line" > < a id = "l00552" name = "l00552" > < / a > < span class = "lineno" > 552< / span > RowIndex diagonal_row,< / div >
< div class = "line" > < a id = "l00553" name = "l00553" > < / a > < span class = "lineno" > 553< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > diagonal_value);< / div >
< div class = "line" > < a id = "l00554" name = "l00554" > < / a > < span class = "lineno" > 554< / span > < span class = "keywordtype" > void< / span > AddDiagonalOnlyColumn(< a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > diagonal_value);< / div >
< div class = "line" > < a id = "l00555" name = "l00555" > < / a > < span class = "lineno" > 555< / span > < / div >
< div class = "line" > < a id = "l00556" name = "l00556" > < / a > < span class = "lineno" > 556< / span > < span class = "comment" > // Adds the given sparse column divided by diagonal_coefficient.< / span > < / div >
< div class = "line" > < a id = "l00557" name = "l00557" > < / a > < span class = "lineno" > 557< / span > < span class = "comment" > // The diagonal_row is assumed to be present and its value should be the< / span > < / div >
< div class = "line" > < a id = "l00558" name = "l00558" > < / a > < span class = "lineno" > 558< / span > < span class = "comment" > // same as the one given in diagonal_coefficient. Note that this function< / span > < / div >
< div class = "line" > < a id = "l00559" name = "l00559" > < / a > < span class = "lineno" > 559< / span > < span class = "comment" > // tests for zero coefficients in the input column and removes them.< / span > < / div >
< div class = "line" > < a id = "l00560" name = "l00560" > < / a > < span class = "lineno" > 560< / span > < span class = "keywordtype" > void< / span > AddAndNormalizeTriangularColumn(< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_column.html" > SparseColumn< / a > & column,< / div >
< div class = "line" > < a id = "l00561" name = "l00561" > < / a > < span class = "lineno" > 561< / span > RowIndex diagonal_row,< / div >
< div class = "line" > < a id = "l00562" name = "l00562" > < / a > < span class = "lineno" > 562< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > diagonal_coefficient);< / div >
< div class = "line" > < a id = "l00563" name = "l00563" > < / a > < span class = "lineno" > 563< / span > < / div >
< div class = "line" > < a id = "l00564" name = "l00564" > < / a > < span class = "lineno" > 564< / span > < span class = "comment" > // Applies the given row permutation to all entries except the diagonal ones.< / span > < / div >
< div class = "line" > < a id = "l00565" name = "l00565" > < / a > < span class = "lineno" > 565< / span > < span class = "keywordtype" > void< / span > ApplyRowPermutationToNonDiagonalEntries(< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_permutation.html" > RowPermutation< / a > & row_perm);< / div >
< div class = "line" > < a id = "l00566" name = "l00566" > < / a > < span class = "lineno" > 566< / span > < / div >
< div class = "line" > < a id = "l00567" name = "l00567" > < / a > < span class = "lineno" > 567< / span > < span class = "comment" > // Copy a triangular column with its diagonal entry to the given SparseColumn.< / span > < / div >
< div class = "line" > < a id = "l00568" name = "l00568" > < / a > < span class = "lineno" > 568< / span > < span class = "keywordtype" > void< / span > CopyColumnToSparseColumn(ColIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > , < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_column.html" > SparseColumn< / a > * output) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00569" name = "l00569" > < / a > < span class = "lineno" > 569< / span > < / div >
< div class = "line" > < a id = "l00570" name = "l00570" > < / a > < span class = "lineno" > 570< / span > < span class = "comment" > // Copy a triangular matrix to the given SparseMatrix.< / span > < / div >
< div class = "line" > < a id = "l00571" name = "l00571" > < / a > < span class = "lineno" > 571< / span > < span class = "keywordtype" > void< / span > CopyToSparseMatrix(< a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_matrix.html" > SparseMatrix< / a > * output) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00572" name = "l00572" > < / a > < span class = "lineno" > 572< / span > < / div >
< div class = "line" > < a id = "l00573" name = "l00573" > < / a > < span class = "lineno" > 573< / span > < span class = "comment" > // Returns the index of the first column which is not an identity column (i.e.< / span > < / div >
< div class = "line" > < a id = "l00574" name = "l00574" > < / a > < span class = "lineno" > 574< / span > < span class = "comment" > // a column j with only one entry of value 1 at the j-th row). This is always< / span > < / div >
< div class = "line" > < a id = "l00575" name = "l00575" > < / a > < span class = "lineno" > 575< / span > < span class = "comment" > // zero if the matrix is not triangular.< / span > < / div >
< div class = "line" > < a id = "l00576" name = "l00576" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#a3d3abc3e6b522dc60fd8c1168522e09d" > 576< / a > < / span > ColIndex < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#a3d3abc3e6b522dc60fd8c1168522e09d" > GetFirstNonIdentityColumn< / a > ()< span class = "keyword" > const < / span > {< / div >
< div class = "line" > < a id = "l00577" name = "l00577" > < / a > < span class = "lineno" > 577< / span > < span class = "keywordflow" > return< / span > first_non_identity_column_;< / div >
< div class = "line" > < a id = "l00578" name = "l00578" > < / a > < span class = "lineno" > 578< / span > }< / div >
< div class = "line" > < a id = "l00579" name = "l00579" > < / a > < span class = "lineno" > 579< / span > < / div >
< div class = "line" > < a id = "l00580" name = "l00580" > < / a > < span class = "lineno" > 580< / span > < span class = "comment" > // Returns the diagonal coefficient of the given column.< / span > < / div >
< div class = "line" > < a id = "l00581" name = "l00581" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#a4a34504e7ba1673cf24f745d162fda84" > 581< / a > < / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#a4a34504e7ba1673cf24f745d162fda84" > GetDiagonalCoefficient< / a > (ColIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > )< span class = "keyword" > const < / span > {< / div >
< div class = "line" > < a id = "l00582" name = "l00582" > < / a > < span class = "lineno" > 582< / span > < span class = "keywordflow" > return< / span > diagonal_coefficients_[< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > ];< / div >
< div class = "line" > < a id = "l00583" name = "l00583" > < / a > < span class = "lineno" > 583< / span > }< / div >
< div class = "line" > < a id = "l00584" name = "l00584" > < / a > < span class = "lineno" > 584< / span > < / div >
< div class = "line" > < a id = "l00585" name = "l00585" > < / a > < span class = "lineno" > 585< / span > < span class = "comment" > // Returns true iff the column contains no non-diagonal entries.< / span > < / div >
< div class = "line" > < a id = "l00586" name = "l00586" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#af0dafb025bcf4174501a93fb91ca4bb6" > 586< / a > < / span > < span class = "keywordtype" > bool< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#af0dafb025bcf4174501a93fb91ca4bb6" > ColumnIsDiagonalOnly< / a > (ColIndex < a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > )< span class = "keyword" > const < / span > {< / div >
< div class = "line" > < a id = "l00587" name = "l00587" > < / a > < span class = "lineno" > 587< / span > < span class = "keywordflow" > return< / span > < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a1426d8ab983ec32193c571f5e8c02cda" > CompactSparseMatrix::ColumnIsEmpty< / a > (< a class = "code hl_variable" href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > );< / div >
< div class = "line" > < a id = "l00588" name = "l00588" > < / a > < span class = "lineno" > 588< / span > }< / div >
< div class = "line" > < a id = "l00589" name = "l00589" > < / a > < span class = "lineno" > 589< / span > < / div >
< div class = "line" > < a id = "l00590" name = "l00590" > < / a > < span class = "lineno" > 590< / span > < span class = "comment" > // --------------------------------------------------------------------------< / span > < / div >
< div class = "line" > < a id = "l00591" name = "l00591" > < / a > < span class = "lineno" > 591< / span > < span class = "comment" > // Triangular solve functions.< / span > < / div >
< div class = "line" > < a id = "l00592" name = "l00592" > < / a > < span class = "lineno" > 592< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00593" name = "l00593" > < / a > < span class = "lineno" > 593< / span > < span class = "comment" > // All the functions containing the word Lower (resp. Upper) require the< / span > < / div >
< div class = "line" > < a id = "l00594" name = "l00594" > < / a > < span class = "lineno" > 594< / span > < span class = "comment" > // matrix to be lower (resp. upper_) triangular without any permutation.< / span > < / div >
< div class = "line" > < a id = "l00595" name = "l00595" > < / a > < span class = "lineno" > 595< / span > < span class = "comment" > // --------------------------------------------------------------------------< / span > < / div >
< div class = "line" > < a id = "l00596" name = "l00596" > < / a > < span class = "lineno" > 596< / span > < / div >
< div class = "line" > < a id = "l00597" name = "l00597" > < / a > < span class = "lineno" > 597< / span > < span class = "comment" > // Solve the system L.x = rhs for a lower triangular matrix.< / span > < / div >
< div class = "line" > < a id = "l00598" name = "l00598" > < / a > < span class = "lineno" > 598< / span > < span class = "comment" > // The result overwrite rhs.< / span > < / div >
< div class = "line" > < a id = "l00599" name = "l00599" > < / a > < span class = "lineno" > 599< / span > < span class = "keywordtype" > void< / span > LowerSolve(< a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > * rhs) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00600" name = "l00600" > < / a > < span class = "lineno" > 600< / span > < / div >
< div class = "line" > < a id = "l00601" name = "l00601" > < / a > < span class = "lineno" > 601< / span > < span class = "comment" > // Solves the system U.x = rhs for an upper triangular matrix.< / span > < / div >
< div class = "line" > < a id = "l00602" name = "l00602" > < / a > < span class = "lineno" > 602< / span > < span class = "keywordtype" > void< / span > UpperSolve(< a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > * rhs) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00603" name = "l00603" > < / a > < span class = "lineno" > 603< / span > < / div >
< div class = "line" > < a id = "l00604" name = "l00604" > < / a > < span class = "lineno" > 604< / span > < span class = "comment" > // Solves the system Transpose(U).x = rhs where U is upper triangular.< / span > < / div >
< div class = "line" > < a id = "l00605" name = "l00605" > < / a > < span class = "lineno" > 605< / span > < span class = "comment" > // This can be used to do a left-solve for a row vector (i.e. y.Y = rhs).< / span > < / div >
< div class = "line" > < a id = "l00606" name = "l00606" > < / a > < span class = "lineno" > 606< / span > < span class = "keywordtype" > void< / span > TransposeUpperSolve(< a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > * rhs) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00607" name = "l00607" > < / a > < span class = "lineno" > 607< / span > < / div >
< div class = "line" > < a id = "l00608" name = "l00608" > < / a > < span class = "lineno" > 608< / span > < span class = "comment" > // This assumes that the rhs is all zero before the given position.< / span > < / div >
< div class = "line" > < a id = "l00609" name = "l00609" > < / a > < span class = "lineno" > 609< / span > < span class = "keywordtype" > void< / span > LowerSolveStartingAt(ColIndex start, < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > * rhs) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00610" name = "l00610" > < / a > < span class = "lineno" > 610< / span > < / div >
< div class = "line" > < a id = "l00611" name = "l00611" > < / a > < span class = "lineno" > 611< / span > < span class = "comment" > // Solves the system Transpose(L).x = rhs, where L is lower triangular.< / span > < / div >
< div class = "line" > < a id = "l00612" name = "l00612" > < / a > < span class = "lineno" > 612< / span > < span class = "comment" > // This can be used to do a left-solve for a row vector (i.e., y.Y = rhs).< / span > < / div >
< div class = "line" > < a id = "l00613" name = "l00613" > < / a > < span class = "lineno" > 613< / span > < span class = "keywordtype" > void< / span > TransposeLowerSolve(< a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > * rhs) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00614" name = "l00614" > < / a > < span class = "lineno" > 614< / span > < / div >
< div class = "line" > < a id = "l00615" name = "l00615" > < / a > < span class = "lineno" > 615< / span > < span class = "comment" > // Hyper-sparse version of the triangular solve functions. The passed< / span > < / div >
< div class = "line" > < a id = "l00616" name = "l00616" > < / a > < span class = "lineno" > 616< / span > < span class = "comment" > // non_zero_rows should contain the positions of the symbolic non-zeros of the< / span > < / div >
< div class = "line" > < a id = "l00617" name = "l00617" > < / a > < span class = "lineno" > 617< / span > < span class = "comment" > // result in the order in which they need to be accessed (or in the reverse< / span > < / div >
< div class = "line" > < a id = "l00618" name = "l00618" > < / a > < span class = "lineno" > 618< / span > < span class = "comment" > // order for the Reverse*() versions).< / span > < / div >
< div class = "line" > < a id = "l00619" name = "l00619" > < / a > < span class = "lineno" > 619< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00620" name = "l00620" > < / a > < span class = "lineno" > 620< / span > < span class = "comment" > // The non-zero vector is mutable so that the symbolic non-zeros that are< / span > < / div >
< div class = "line" > < a id = "l00621" name = "l00621" > < / a > < span class = "lineno" > 621< / span > < span class = "comment" > // actually zero because of numerical cancellations can be removed.< / span > < / div >
< div class = "line" > < a id = "l00622" name = "l00622" > < / a > < span class = "lineno" > 622< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00623" name = "l00623" > < / a > < span class = "lineno" > 623< / span > < span class = "comment" > // The non-zeros can be computed by one of these two methods:< / span > < / div >
< div class = "line" > < a id = "l00624" name = "l00624" > < / a > < span class = "lineno" > 624< / span > < span class = "comment" > // - ComputeRowsToConsiderWithDfs() which will give them in the reverse order< / span > < / div >
< div class = "line" > < a id = "l00625" name = "l00625" > < / a > < span class = "lineno" > 625< / span > < span class = "comment" > // of the one they need to be accessed in. This is only a topological order,< / span > < / div >
< div class = "line" > < a id = "l00626" name = "l00626" > < / a > < span class = "lineno" > 626< / span > < span class = "comment" > // and it will not necessarily be " sorted" .< / span > < / div >
< div class = "line" > < a id = "l00627" name = "l00627" > < / a > < span class = "lineno" > 627< / span > < span class = "comment" > // - ComputeRowsToConsiderInSortedOrder() which will always give them in< / span > < / div >
< div class = "line" > < a id = "l00628" name = "l00628" > < / a > < span class = "lineno" > 628< / span > < span class = "comment" > // increasing order.< / span > < / div >
< div class = "line" > < a id = "l00629" name = "l00629" > < / a > < span class = "lineno" > 629< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00630" name = "l00630" > < / a > < span class = "lineno" > 630< / span > < span class = "comment" > // Note that if the non-zeros are given in a sorted order, then the< / span > < / div >
< div class = "line" > < a id = "l00631" name = "l00631" > < / a > < span class = "lineno" > 631< / span > < span class = "comment" > // hyper-sparse functions will return EXACTLY the same results as the non< / span > < / div >
< div class = "line" > < a id = "l00632" name = "l00632" > < / a > < span class = "lineno" > 632< / span > < span class = "comment" > // hyper-sparse version above.< / span > < / div >
< div class = "line" > < a id = "l00633" name = "l00633" > < / a > < span class = "lineno" > 633< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00634" name = "l00634" > < / a > < span class = "lineno" > 634< / span > < span class = "comment" > // For a given solve, here is the required order:< / span > < / div >
< div class = "line" > < a id = "l00635" name = "l00635" > < / a > < span class = "lineno" > 635< / span > < span class = "comment" > // - For a lower solve, increasing non-zeros order.< / span > < / div >
< div class = "line" > < a id = "l00636" name = "l00636" > < / a > < span class = "lineno" > 636< / span > < span class = "comment" > // - For an upper solve, decreasing non-zeros order.< / span > < / div >
< div class = "line" > < a id = "l00637" name = "l00637" > < / a > < span class = "lineno" > 637< / span > < span class = "comment" > // - for a transpose lower solve, decreasing non-zeros order.< / span > < / div >
< div class = "line" > < a id = "l00638" name = "l00638" > < / a > < span class = "lineno" > 638< / span > < span class = "comment" > // - for a transpose upper solve, increasing non_zeros order.< / span > < / div >
< div class = "line" > < a id = "l00639" name = "l00639" > < / a > < span class = "lineno" > 639< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00640" name = "l00640" > < / a > < span class = "lineno" > 640< / span > < span class = "comment" > // For a general discussion of hyper-sparsity in LP, see:< / span > < / div >
< div class = "line" > < a id = "l00641" name = "l00641" > < / a > < span class = "lineno" > 641< / span > < span class = "comment" > // J.A.J. Hall, K.I.M. McKinnon, " Exploiting hyper-sparsity in the revised< / span > < / div >
< div class = "line" > < a id = "l00642" name = "l00642" > < / a > < span class = "lineno" > 642< / span > < span class = "comment" > // simplex method" , December 1999, MS 99-014.< / span > < / div >
< div class = "line" > < a id = "l00643" name = "l00643" > < / a > < span class = "lineno" > 643< / span > < span class = "comment" > // http://www.maths.ed.ac.uk/hall/MS-99/MS9914.pdf< / span > < / div >
< div class = "line" > < a id = "l00644" name = "l00644" > < / a > < span class = "lineno" > 644< / span > < span class = "keywordtype" > void< / span > HyperSparseSolve(< a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > * rhs, < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#ac014de658aabf122011e8fb07b6f4612" > RowIndexVector< / a > * non_zero_rows) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00645" name = "l00645" > < / a > < span class = "lineno" > 645< / span > < span class = "keywordtype" > void< / span > HyperSparseSolveWithReversedNonZeros(< / div >
< div class = "line" > < a id = "l00646" name = "l00646" > < / a > < span class = "lineno" > 646< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > * rhs, < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#ac014de658aabf122011e8fb07b6f4612" > RowIndexVector< / a > * non_zero_rows) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00647" name = "l00647" > < / a > < span class = "lineno" > 647< / span > < span class = "keywordtype" > void< / span > TransposeHyperSparseSolve(< a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > * rhs,< / div >
< div class = "line" > < a id = "l00648" name = "l00648" > < / a > < span class = "lineno" > 648< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#ac014de658aabf122011e8fb07b6f4612" > RowIndexVector< / a > * non_zero_rows) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00649" name = "l00649" > < / a > < span class = "lineno" > 649< / span > < span class = "keywordtype" > void< / span > TransposeHyperSparseSolveWithReversedNonZeros(< / div >
< div class = "line" > < a id = "l00650" name = "l00650" > < / a > < span class = "lineno" > 650< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > * rhs, < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#ac014de658aabf122011e8fb07b6f4612" > RowIndexVector< / a > * non_zero_rows) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00651" name = "l00651" > < / a > < span class = "lineno" > 651< / span > < / div >
< div class = "line" > < a id = "l00652" name = "l00652" > < / a > < span class = "lineno" > 652< / span > < span class = "comment" > // Given the positions of the non-zeros of a vector, computes the non-zero< / span > < / div >
< div class = "line" > < a id = "l00653" name = "l00653" > < / a > < span class = "lineno" > 653< / span > < span class = "comment" > // positions of the vector after a solve by this triangular matrix. The order< / span > < / div >
< div class = "line" > < a id = "l00654" name = "l00654" > < / a > < span class = "lineno" > 654< / span > < span class = "comment" > // of the returned non-zero positions will be in the REVERSE elimination< / span > < / div >
< div class = "line" > < a id = "l00655" name = "l00655" > < / a > < span class = "lineno" > 655< / span > < span class = "comment" > // order. If the function detects that there are too many non-zeros, then it< / span > < / div >
< div class = "line" > < a id = "l00656" name = "l00656" > < / a > < span class = "lineno" > 656< / span > < span class = "comment" > // aborts early and non_zero_rows is cleared.< / span > < / div >
< div class = "line" > < a id = "l00657" name = "l00657" > < / a > < span class = "lineno" > 657< / span > < span class = "keywordtype" > void< / span > ComputeRowsToConsiderWithDfs(< a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#ac014de658aabf122011e8fb07b6f4612" > RowIndexVector< / a > * non_zero_rows) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00658" name = "l00658" > < / a > < span class = "lineno" > 658< / span > < / div >
< div class = "line" > < a id = "l00659" name = "l00659" > < / a > < span class = "lineno" > 659< / span > < span class = "comment" > // Same as TriangularComputeRowsToConsider() but always returns the non-zeros< / span > < / div >
< div class = "line" > < a id = "l00660" name = "l00660" > < / a > < span class = "lineno" > 660< / span > < span class = "comment" > // sorted by rows. It is up to the client to call the direct or reverse< / span > < / div >
< div class = "line" > < a id = "l00661" name = "l00661" > < / a > < span class = "lineno" > 661< / span > < span class = "comment" > // hyper-sparse solve function depending if the matrix is upper or lower< / span > < / div >
< div class = "line" > < a id = "l00662" name = "l00662" > < / a > < span class = "lineno" > 662< / span > < span class = "comment" > // triangular.< / span > < / div >
< div class = "line" > < a id = "l00663" name = "l00663" > < / a > < span class = "lineno" > 663< / span > < span class = "keywordtype" > void< / span > ComputeRowsToConsiderInSortedOrder(< a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#ac014de658aabf122011e8fb07b6f4612" > RowIndexVector< / a > * non_zero_rows,< / div >
< div class = "line" > < a id = "l00664" name = "l00664" > < / a > < span class = "lineno" > 664< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > sparsity_ratio,< / div >
< div class = "line" > < a id = "l00665" name = "l00665" > < / a > < span class = "lineno" > 665< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > num_ops_ratio) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00666" name = "l00666" > < / a > < span class = "lineno" > 666< / span > < span class = "keywordtype" > void< / span > ComputeRowsToConsiderInSortedOrder(< a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#ac014de658aabf122011e8fb07b6f4612" > RowIndexVector< / a > * non_zero_rows) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00667" name = "l00667" > < / a > < span class = "lineno" > 667< / span > < span class = "comment" > // This is currently only used for testing. It achieves the same result as< / span > < / div >
< div class = "line" > < a id = "l00668" name = "l00668" > < / a > < span class = "lineno" > 668< / span > < span class = "comment" > // PermutedLowerSparseSolve() below, but the latter exploits the sparsity of< / span > < / div >
< div class = "line" > < a id = "l00669" name = "l00669" > < / a > < span class = "lineno" > 669< / span > < span class = "comment" > // rhs and is thus faster for our use case.< / span > < / div >
< div class = "line" > < a id = "l00670" name = "l00670" > < / a > < span class = "lineno" > 670< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00671" name = "l00671" > < / a > < span class = "lineno" > 671< / span > < span class = "comment" > // Note that partial_inverse_row_perm only permutes the first k rows, where k< / span > < / div >
< div class = "line" > < a id = "l00672" name = "l00672" > < / a > < span class = "lineno" > 672< / span > < span class = "comment" > // is the same as partial_inverse_row_perm.size(). It is the inverse< / span > < / div >
< div class = "line" > < a id = "l00673" name = "l00673" > < / a > < span class = "lineno" > 673< / span > < span class = "comment" > // permutation of row_perm which only permutes k rows into is [0, k), the< / span > < / div >
< div class = "line" > < a id = "l00674" name = "l00674" > < / a > < span class = "lineno" > 674< / span > < span class = "comment" > // other row images beeing kInvalidRow. The other arguments are the same as< / span > < / div >
< div class = "line" > < a id = "l00675" name = "l00675" > < / a > < span class = "lineno" > 675< / span > < span class = "comment" > // for PermutedLowerSparseSolve() and described there.< / span > < / div >
< div class = "line" > < a id = "l00676" name = "l00676" > < / a > < span class = "lineno" > 676< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00677" name = "l00677" > < / a > < span class = "lineno" > 677< / span > < span class = "comment" > // IMPORTANT: lower will contain all the " symbolic" non-zero entries.< / span > < / div >
< div class = "line" > < a id = "l00678" name = "l00678" > < / a > < span class = "lineno" > 678< / span > < span class = "comment" > // A " symbolic" zero entry is one that will be zero whatever the coefficients< / span > < / div >
< div class = "line" > < a id = "l00679" name = "l00679" > < / a > < span class = "lineno" > 679< / span > < span class = "comment" > // of the rhs entries. That is it only depends on the position of its< / span > < / div >
< div class = "line" > < a id = "l00680" name = "l00680" > < / a > < span class = "lineno" > 680< / span > < span class = "comment" > // entries, not on their values. Thus, some of its coefficients may be zero.< / span > < / div >
< div class = "line" > < a id = "l00681" name = "l00681" > < / a > < span class = "lineno" > 681< / span > < span class = "comment" > // This fact is exploited by the LU factorization code. The zero coefficients< / span > < / div >
< div class = "line" > < a id = "l00682" name = "l00682" > < / a > < span class = "lineno" > 682< / span > < span class = "comment" > // of upper will be cleaned, however.< / span > < / div >
< div class = "line" > < a id = "l00683" name = "l00683" > < / a > < span class = "lineno" > 683< / span > < span class = "keywordtype" > void< / span > PermutedLowerSolve(< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_column.html" > SparseColumn< / a > & rhs,< / div >
< div class = "line" > < a id = "l00684" name = "l00684" > < / a > < span class = "lineno" > 684< / span > < span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_permutation.html" > RowPermutation< / a > & row_perm,< / div >
< div class = "line" > < a id = "l00685" name = "l00685" > < / a > < span class = "lineno" > 685< / span > < span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > RowMapping< / a > & partial_inverse_row_perm,< / div >
< div class = "line" > < a id = "l00686" name = "l00686" > < / a > < span class = "lineno" > 686< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_column.html" > SparseColumn< / a > * lower, < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_column.html" > SparseColumn< / a > * upper) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00687" name = "l00687" > < / a > < span class = "lineno" > 687< / span > < / div >
< div class = "line" > < a id = "l00688" name = "l00688" > < / a > < span class = "lineno" > 688< / span > < span class = "comment" > // This solves a lower triangular system with only ones on the diagonal where< / span > < / div >
< div class = "line" > < a id = "l00689" name = "l00689" > < / a > < span class = "lineno" > 689< / span > < span class = "comment" > // the matrix and the input rhs are permuted by the inverse of row_perm. Note< / span > < / div >
< div class = "line" > < a id = "l00690" name = "l00690" > < / a > < span class = "lineno" > 690< / span > < span class = "comment" > // that the output will also be permuted by the inverse of row_perm. The< / span > < / div >
< div class = "line" > < a id = "l00691" name = "l00691" > < / a > < span class = "lineno" > 691< / span > < span class = "comment" > // function also supports partial permutation. That is if row_perm[i] < 0 then< / span > < / div >
< div class = "line" > < a id = "l00692" name = "l00692" > < / a > < span class = "lineno" > 692< / span > < span class = "comment" > // column row_perm[i] is assumed to be an identity column.< / span > < / div >
< div class = "line" > < a id = "l00693" name = "l00693" > < / a > < span class = "lineno" > 693< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00694" name = "l00694" > < / a > < span class = "lineno" > 694< / span > < span class = "comment" > // The output is given as follow:< / span > < / div >
< div class = "line" > < a id = "l00695" name = "l00695" > < / a > < span class = "lineno" > 695< / span > < span class = "comment" > // - lower is cleared, and receives the rows for which row_perm[row] < 0< / span > < / div >
< div class = "line" > < a id = "l00696" name = "l00696" > < / a > < span class = "lineno" > 696< / span > < span class = "comment" > // meaning not yet examined as a pivot (see markowitz.cc).< / span > < / div >
< div class = "line" > < a id = "l00697" name = "l00697" > < / a > < span class = "lineno" > 697< / span > < span class = "comment" > // - upper is NOT cleared, and the other rows (row_perm[row] > = 0) are< / span > < / div >
< div class = "line" > < a id = "l00698" name = "l00698" > < / a > < span class = "lineno" > 698< / span > < span class = "comment" > // appended to it.< / span > < / div >
< div class = "line" > < a id = "l00699" name = "l00699" > < / a > < span class = "lineno" > 699< / span > < span class = "comment" > // - Note that lower and upper can point to the same SparseColumn.< / span > < / div >
< div class = "line" > < a id = "l00700" name = "l00700" > < / a > < span class = "lineno" > 700< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00701" name = "l00701" > < / a > < span class = "lineno" > 701< / span > < span class = "comment" > // Note: This function is non-const because ComputeRowsToConsider() also< / span > < / div >
< div class = "line" > < a id = "l00702" name = "l00702" > < / a > < span class = "lineno" > 702< / span > < span class = "comment" > // prunes the underlying dependency graph of the lower matrix while doing a< / span > < / div >
< div class = "line" > < a id = "l00703" name = "l00703" > < / a > < span class = "lineno" > 703< / span > < span class = "comment" > // solve. See marked_ and pruned_ends_ below.< / span > < / div >
< div class = "line" > < a id = "l00704" name = "l00704" > < / a > < span class = "lineno" > 704< / span > < span class = "keywordtype" > void< / span > PermutedLowerSparseSolve(< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_column_view.html" > ColumnView< / a > & rhs,< / div >
< div class = "line" > < a id = "l00705" name = "l00705" > < / a > < span class = "lineno" > 705< / span > < span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_permutation.html" > RowPermutation< / a > & row_perm,< / div >
< div class = "line" > < a id = "l00706" name = "l00706" > < / a > < span class = "lineno" > 706< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_column.html" > SparseColumn< / a > * lower, < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_sparse_column.html" > SparseColumn< / a > * upper);< / div >
< div class = "line" > < a id = "l00707" name = "l00707" > < / a > < span class = "lineno" > 707< / span > < / div >
< div class = "line" > < a id = "l00708" name = "l00708" > < / a > < span class = "lineno" > 708< / span > < span class = "comment" > // This is used to compute the deterministic time of a matrix factorization.< / span > < / div >
< div class = "line" > < a id = "l00709" name = "l00709" > < / a > < span class = "lineno" > < a class = "line" href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#aec8942e8b01f9aed2abc24de9acfb6ab" > 709< / a > < / span > int64_t < a class = "code hl_function" href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#aec8942e8b01f9aed2abc24de9acfb6ab" > NumFpOperationsInLastPermutedLowerSparseSolve< / a > ()< span class = "keyword" > const < / span > {< / div >
< div class = "line" > < a id = "l00710" name = "l00710" > < / a > < span class = "lineno" > 710< / span > < span class = "keywordflow" > return< / span > num_fp_operations_;< / div >
< div class = "line" > < a id = "l00711" name = "l00711" > < / a > < span class = "lineno" > 711< / span > }< / div >
< div class = "line" > < a id = "l00712" name = "l00712" > < / a > < span class = "lineno" > 712< / span > < / div >
< div class = "line" > < a id = "l00713" name = "l00713" > < / a > < span class = "lineno" > 713< / span > < span class = "comment" > // To be used in DEBUG mode by the client code. This check that the matrix is< / span > < / div >
< div class = "line" > < a id = "l00714" name = "l00714" > < / a > < span class = "lineno" > 714< / span > < span class = "comment" > // lower- (resp. upper-) triangular without any permutation and that there is< / span > < / div >
< div class = "line" > < a id = "l00715" name = "l00715" > < / a > < span class = "lineno" > 715< / span > < span class = "comment" > // no zero on the diagonal. We can' t do that on each Solve() that require so,< / span > < / div >
< div class = "line" > < a id = "l00716" name = "l00716" > < / a > < span class = "lineno" > 716< / span > < span class = "comment" > // otherwise it will be too slow in debug.< / span > < / div >
< div class = "line" > < a id = "l00717" name = "l00717" > < / a > < span class = "lineno" > 717< / span > < span class = "keywordtype" > bool< / span > IsLowerTriangular() < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00718" name = "l00718" > < / a > < span class = "lineno" > 718< / span > < span class = "keywordtype" > bool< / span > IsUpperTriangular() < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00719" name = "l00719" > < / a > < span class = "lineno" > 719< / span > < / div >
< div class = "line" > < a id = "l00720" name = "l00720" > < / a > < span class = "lineno" > 720< / span > < span class = "comment" > // Visible for testing. This is used by PermutedLowerSparseSolve() to compute< / span > < / div >
< div class = "line" > < a id = "l00721" name = "l00721" > < / a > < span class = "lineno" > 721< / span > < span class = "comment" > // the non-zero indices of the result. The output is as follow:< / span > < / div >
< div class = "line" > < a id = "l00722" name = "l00722" > < / a > < span class = "lineno" > 722< / span > < span class = "comment" > // - lower_column_rows will contains the rows for which row_perm[row] < 0.< / span > < / div >
< div class = "line" > < a id = "l00723" name = "l00723" > < / a > < span class = "lineno" > 723< / span > < span class = "comment" > // - upper_column_rows will contains the other rows in the reverse topological< / span > < / div >
< div class = "line" > < a id = "l00724" name = "l00724" > < / a > < span class = "lineno" > 724< / span > < span class = "comment" > // order in which they should be considered in PermutedLowerSparseSolve().< / span > < / div >
< div class = "line" > < a id = "l00725" name = "l00725" > < / a > < span class = "lineno" > 725< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00726" name = "l00726" > < / a > < span class = "lineno" > 726< / span > < span class = "comment" > // This function is non-const because it prunes the underlying dependency< / span > < / div >
< div class = "line" > < a id = "l00727" name = "l00727" > < / a > < span class = "lineno" > 727< / span > < span class = "comment" > // graph of the lower matrix while doing a solve. See marked_ and pruned_ends_< / span > < / div >
< div class = "line" > < a id = "l00728" name = "l00728" > < / a > < span class = "lineno" > 728< / span > < span class = "comment" > // below.< / span > < / div >
< div class = "line" > < a id = "l00729" name = "l00729" > < / a > < span class = "lineno" > 729< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00730" name = "l00730" > < / a > < span class = "lineno" > 730< / span > < span class = "comment" > // Pruning the graph at the same time is slower but not by too much (< 2x) and< / span > < / div >
< div class = "line" > < a id = "l00731" name = "l00731" > < / a > < span class = "lineno" > 731< / span > < span class = "comment" > // seems worth doing. Note that when the lower matrix is dense, most of the< / span > < / div >
< div class = "line" > < a id = "l00732" name = "l00732" > < / a > < span class = "lineno" > 732< / span > < span class = "comment" > // graph will likely be pruned. As a result, the symbolic phase will be< / span > < / div >
< div class = "line" > < a id = "l00733" name = "l00733" > < / a > < span class = "lineno" > 733< / span > < span class = "comment" > // negligible compared to the numerical phase so we don' t really need a dense< / span > < / div >
< div class = "line" > < a id = "l00734" name = "l00734" > < / a > < span class = "lineno" > 734< / span > < span class = "comment" > // version of PermutedLowerSparseSolve().< / span > < / div >
< div class = "line" > < a id = "l00735" name = "l00735" > < / a > < span class = "lineno" > 735< / span > < span class = "keywordtype" > void< / span > PermutedComputeRowsToConsider(< span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_column_view.html" > ColumnView< / a > & rhs,< / div >
< div class = "line" > < a id = "l00736" name = "l00736" > < / a > < span class = "lineno" > 736< / span > < span class = "keyword" > const< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_permutation.html" > RowPermutation< / a > & row_perm,< / div >
< div class = "line" > < a id = "l00737" name = "l00737" > < / a > < span class = "lineno" > 737< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#ac014de658aabf122011e8fb07b6f4612" > RowIndexVector< / a > * lower_column_rows,< / div >
< div class = "line" > < a id = "l00738" name = "l00738" > < / a > < span class = "lineno" > 738< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#ac014de658aabf122011e8fb07b6f4612" > RowIndexVector< / a > * upper_column_rows);< / div >
< div class = "line" > < a id = "l00739" name = "l00739" > < / a > < span class = "lineno" > 739< / span > < / div >
< div class = "line" > < a id = "l00740" name = "l00740" > < / a > < span class = "lineno" > 740< / span > < span class = "comment" > // The upper bound is computed using one of the algorithm presented in< / span > < / div >
< div class = "line" > < a id = "l00741" name = "l00741" > < / a > < span class = "lineno" > 741< / span > < span class = "comment" > // " A Survey of Condition Number Estimation for Triangular Matrices" < / span > < / div >
< div class = "line" > < a id = "l00742" name = "l00742" > < / a > < span class = "lineno" > 742< / span > < span class = "comment" > // https:epubs.siam.org/doi/pdf/10.1137/1029112/< / span > < / div >
< div class = "line" > < a id = "l00743" name = "l00743" > < / a > < span class = "lineno" > 743< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > ComputeInverseInfinityNormUpperBound() < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00744" name = "l00744" > < / a > < span class = "lineno" > 744< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > ComputeInverseInfinityNorm() < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00745" name = "l00745" > < / a > < span class = "lineno" > 745< / span > < / div >
< div class = "line" > < a id = "l00746" name = "l00746" > < / a > < span class = "lineno" > 746< / span > < span class = "keyword" > private< / span > :< / div >
< div class = "line" > < a id = "l00747" name = "l00747" > < / a > < span class = "lineno" > 747< / span > < span class = "comment" > // Internal versions of some Solve() functions to avoid code duplication.< / span > < / div >
< div class = "line" > < a id = "l00748" name = "l00748" > < / a > < span class = "lineno" > 748< / span > < span class = "keyword" > template< / span > < < span class = "keywordtype" > bool< / span > diagonal_of_ones> < / div >
< div class = "line" > < a id = "l00749" name = "l00749" > < / a > < span class = "lineno" > 749< / span > < span class = "keywordtype" > void< / span > LowerSolveStartingAtInternal(ColIndex start, < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > * rhs) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00750" name = "l00750" > < / a > < span class = "lineno" > 750< / span > < span class = "keyword" > template< / span > < < span class = "keywordtype" > bool< / span > diagonal_of_ones> < / div >
< div class = "line" > < a id = "l00751" name = "l00751" > < / a > < span class = "lineno" > 751< / span > < span class = "keywordtype" > void< / span > UpperSolveInternal(< a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > * rhs) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00752" name = "l00752" > < / a > < span class = "lineno" > 752< / span > < span class = "keyword" > template< / span > < < span class = "keywordtype" > bool< / span > diagonal_of_ones> < / div >
< div class = "line" > < a id = "l00753" name = "l00753" > < / a > < span class = "lineno" > 753< / span > < span class = "keywordtype" > void< / span > TransposeLowerSolveInternal(< a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > * rhs) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00754" name = "l00754" > < / a > < span class = "lineno" > 754< / span > < span class = "keyword" > template< / span > < < span class = "keywordtype" > bool< / span > diagonal_of_ones> < / div >
< div class = "line" > < a id = "l00755" name = "l00755" > < / a > < span class = "lineno" > 755< / span > < span class = "keywordtype" > void< / span > TransposeUpperSolveInternal(< a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > * rhs) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00756" name = "l00756" > < / a > < span class = "lineno" > 756< / span > < span class = "keyword" > template< / span > < < span class = "keywordtype" > bool< / span > diagonal_of_ones> < / div >
< div class = "line" > < a id = "l00757" name = "l00757" > < / a > < span class = "lineno" > 757< / span > < span class = "keywordtype" > void< / span > HyperSparseSolveInternal(< a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > * rhs,< / div >
< div class = "line" > < a id = "l00758" name = "l00758" > < / a > < span class = "lineno" > 758< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#ac014de658aabf122011e8fb07b6f4612" > RowIndexVector< / a > * non_zero_rows) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00759" name = "l00759" > < / a > < span class = "lineno" > 759< / span > < span class = "keyword" > template< / span > < < span class = "keywordtype" > bool< / span > diagonal_of_ones> < / div >
< div class = "line" > < a id = "l00760" name = "l00760" > < / a > < span class = "lineno" > 760< / span > < span class = "keywordtype" > void< / span > HyperSparseSolveWithReversedNonZerosInternal(< / div >
< div class = "line" > < a id = "l00761" name = "l00761" > < / a > < span class = "lineno" > 761< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > * rhs, < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#ac014de658aabf122011e8fb07b6f4612" > RowIndexVector< / a > * non_zero_rows) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00762" name = "l00762" > < / a > < span class = "lineno" > 762< / span > < span class = "keyword" > template< / span > < < span class = "keywordtype" > bool< / span > diagonal_of_ones> < / div >
< div class = "line" > < a id = "l00763" name = "l00763" > < / a > < span class = "lineno" > 763< / span > < span class = "keywordtype" > void< / span > TransposeHyperSparseSolveInternal(< a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > * rhs,< / div >
< div class = "line" > < a id = "l00764" name = "l00764" > < / a > < span class = "lineno" > 764< / span > < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#ac014de658aabf122011e8fb07b6f4612" > RowIndexVector< / a > * non_zero_rows) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00765" name = "l00765" > < / a > < span class = "lineno" > 765< / span > < span class = "keyword" > template< / span > < < span class = "keywordtype" > bool< / span > diagonal_of_ones> < / div >
< div class = "line" > < a id = "l00766" name = "l00766" > < / a > < span class = "lineno" > 766< / span > < span class = "keywordtype" > void< / span > TransposeHyperSparseSolveWithReversedNonZerosInternal(< / div >
< div class = "line" > < a id = "l00767" name = "l00767" > < / a > < span class = "lineno" > 767< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > * rhs, < a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#ac014de658aabf122011e8fb07b6f4612" > RowIndexVector< / a > * non_zero_rows) < span class = "keyword" > const< / span > ;< / div >
< div class = "line" > < a id = "l00768" name = "l00768" > < / a > < span class = "lineno" > 768< / span > < / div >
< div class = "line" > < a id = "l00769" name = "l00769" > < / a > < span class = "lineno" > 769< / span > < span class = "comment" > // Internal function used by the Add*() functions to finish adding< / span > < / div >
< div class = "line" > < a id = "l00770" name = "l00770" > < / a > < span class = "lineno" > 770< / span > < span class = "comment" > // a new column to a triangular matrix.< / span > < / div >
< div class = "line" > < a id = "l00771" name = "l00771" > < / a > < span class = "lineno" > 771< / span > < span class = "keywordtype" > void< / span > CloseCurrentColumn(< a class = "code hl_typedef" href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > Fractional< / a > diagonal_value);< / div >
< div class = "line" > < a id = "l00772" name = "l00772" > < / a > < span class = "lineno" > 772< / span > < / div >
< div class = "line" > < a id = "l00773" name = "l00773" > < / a > < span class = "lineno" > 773< / span > < span class = "comment" > // Extra data for " triangular" matrices. The diagonal coefficients are< / span > < / div >
< div class = "line" > < a id = "l00774" name = "l00774" > < / a > < span class = "lineno" > 774< / span > < span class = "comment" > // stored in a separate vector instead of beeing stored in each column.< / span > < / div >
< div class = "line" > < a id = "l00775" name = "l00775" > < / a > < span class = "lineno" > 775< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > StrictITIVector< ColIndex, Fractional> < / a > diagonal_coefficients_;< / div >
< div class = "line" > < a id = "l00776" name = "l00776" > < / a > < span class = "lineno" > 776< / span > < / div >
< div class = "line" > < a id = "l00777" name = "l00777" > < / a > < span class = "lineno" > 777< / span > < span class = "comment" > // Index of the first column which is not a diagonal only column with a< / span > < / div >
< div class = "line" > < a id = "l00778" name = "l00778" > < / a > < span class = "lineno" > 778< / span > < span class = "comment" > // coefficient of 1. This is used to optimize the solves.< / span > < / div >
< div class = "line" > < a id = "l00779" name = "l00779" > < / a > < span class = "lineno" > 779< / span > ColIndex first_non_identity_column_;< / div >
< div class = "line" > < a id = "l00780" name = "l00780" > < / a > < span class = "lineno" > 780< / span > < / div >
< div class = "line" > < a id = "l00781" name = "l00781" > < / a > < span class = "lineno" > 781< / span > < span class = "comment" > // This common case allows for more efficient Solve() functions.< / span > < / div >
< div class = "line" > < a id = "l00782" name = "l00782" > < / a > < span class = "lineno" > 782< / span > < span class = "comment" > // TODO(user): Do not even construct diagonal_coefficients_ in this case?< / span > < / div >
< div class = "line" > < a id = "l00783" name = "l00783" > < / a > < span class = "lineno" > 783< / span > < span class = "keywordtype" > bool< / span > all_diagonal_coefficients_are_one_;< / div >
< div class = "line" > < a id = "l00784" name = "l00784" > < / a > < span class = "lineno" > 784< / span > < / div >
< div class = "line" > < a id = "l00785" name = "l00785" > < / a > < span class = "lineno" > 785< / span > < span class = "comment" > // For the hyper-sparse version. These are used to implement a DFS, see< / span > < / div >
< div class = "line" > < a id = "l00786" name = "l00786" > < / a > < span class = "lineno" > 786< / span > < span class = "comment" > // TriangularComputeRowsToConsider() for more details.< / span > < / div >
< div class = "line" > < a id = "l00787" name = "l00787" > < / a > < span class = "lineno" > 787< / span > < span class = "keyword" > mutable< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseBooleanColumn< / a > stored_;< / div >
< div class = "line" > < a id = "l00788" name = "l00788" > < / a > < span class = "lineno" > 788< / span > < span class = "keyword" > mutable< / span > std::vector< RowIndex> nodes_to_explore_;< / div >
< div class = "line" > < a id = "l00789" name = "l00789" > < / a > < span class = "lineno" > 789< / span > < / div >
< div class = "line" > < a id = "l00790" name = "l00790" > < / a > < span class = "lineno" > 790< / span > < span class = "comment" > // For PermutedLowerSparseSolve().< / span > < / div >
< div class = "line" > < a id = "l00791" name = "l00791" > < / a > < span class = "lineno" > 791< / span > int64_t num_fp_operations_;< / div >
< div class = "line" > < a id = "l00792" name = "l00792" > < / a > < span class = "lineno" > 792< / span > < span class = "keyword" > mutable< / span > std::vector< RowIndex> lower_column_rows_;< / div >
< div class = "line" > < a id = "l00793" name = "l00793" > < / a > < span class = "lineno" > 793< / span > < span class = "keyword" > mutable< / span > std::vector< RowIndex> upper_column_rows_;< / div >
< div class = "line" > < a id = "l00794" name = "l00794" > < / a > < span class = "lineno" > 794< / span > < span class = "keyword" > mutable< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseColumn< / a > initially_all_zero_scratchpad_;< / div >
< div class = "line" > < a id = "l00795" name = "l00795" > < / a > < span class = "lineno" > 795< / span > < / div >
< div class = "line" > < a id = "l00796" name = "l00796" > < / a > < span class = "lineno" > 796< / span > < span class = "comment" > // This boolean vector is used to detect entries that can be pruned during< / span > < / div >
< div class = "line" > < a id = "l00797" name = "l00797" > < / a > < span class = "lineno" > 797< / span > < span class = "comment" > // the DFS used for the symbolic phase of ComputeRowsToConsider().< / span > < / div >
< div class = "line" > < a id = "l00798" name = "l00798" > < / a > < span class = "lineno" > 798< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00799" name = "l00799" > < / a > < span class = "lineno" > 799< / span > < span class = "comment" > // Problem: We have a DAG where each node has outgoing arcs towards other< / span > < / div >
< div class = "line" > < a id = "l00800" name = "l00800" > < / a > < span class = "lineno" > 800< / span > < span class = "comment" > // nodes (this adjacency list is NOT sorted by any order). We want to compute< / span > < / div >
< div class = "line" > < a id = "l00801" name = "l00801" > < / a > < span class = "lineno" > 801< / span > < span class = "comment" > // the reachability of a set of nodes S and its topological order. While doing< / span > < / div >
< div class = "line" > < a id = "l00802" name = "l00802" > < / a > < span class = "lineno" > 802< / span > < span class = "comment" > // this, we also want to prune the adjacency lists to exploit the simple fact< / span > < / div >
< div class = "line" > < a id = "l00803" name = "l00803" > < / a > < span class = "lineno" > 803< / span > < span class = "comment" > // that if a -> (b, c) and b -> (c) then c can be removed from the adjacency< / span > < / div >
< div class = "line" > < a id = "l00804" name = "l00804" > < / a > < span class = "lineno" > 804< / span > < span class = "comment" > // list of a since it will be implied through b. Note that this doesn' t change< / span > < / div >
< div class = "line" > < a id = "l00805" name = "l00805" > < / a > < span class = "lineno" > 805< / span > < span class = "comment" > // the reachability of any set nor a valid topological ordering of such a set.< / span > < / div >
< div class = "line" > < a id = "l00806" name = "l00806" > < / a > < span class = "lineno" > 806< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00807" name = "l00807" > < / a > < span class = "lineno" > 807< / span > < span class = "comment" > // The concept is known as the transitive reduction of a DAG, see< / span > < / div >
< div class = "line" > < a id = "l00808" name = "l00808" > < / a > < span class = "lineno" > 808< / span > < span class = "comment" > // http://en.wikipedia.org/wiki/Transitive_reduction.< / span > < / div >
< div class = "line" > < a id = "l00809" name = "l00809" > < / a > < span class = "lineno" > 809< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00810" name = "l00810" > < / a > < span class = "lineno" > 810< / span > < span class = "comment" > // Heuristic algorithm: While doing the DFS to compute Reach(S) and its< / span > < / div >
< div class = "line" > < a id = "l00811" name = "l00811" > < / a > < span class = "lineno" > 811< / span > < span class = "comment" > // topological order, each time we process a node, we mark all its adjacent< / span > < / div >
< div class = "line" > < a id = "l00812" name = "l00812" > < / a > < span class = "lineno" > 812< / span > < span class = "comment" > // node while going down in the DFS, and then we unmark all of them when we go< / span > < / div >
< div class = "line" > < a id = "l00813" name = "l00813" > < / a > < span class = "lineno" > 813< / span > < span class = "comment" > // back up. During the un-marking, if a node is already un-marked, it means< / span > < / div >
< div class = "line" > < a id = "l00814" name = "l00814" > < / a > < span class = "lineno" > 814< / span > < span class = "comment" > // that it was implied by some other path starting at the current node and we< / span > < / div >
< div class = "line" > < a id = "l00815" name = "l00815" > < / a > < span class = "lineno" > 815< / span > < span class = "comment" > // can prune it and remove it from the adjacency list of the current node.< / span > < / div >
< div class = "line" > < a id = "l00816" name = "l00816" > < / a > < span class = "lineno" > 816< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00817" name = "l00817" > < / a > < span class = "lineno" > 817< / span > < span class = "comment" > // Note(user): I couldn' t find any reference for this algorithm, even though< / span > < / div >
< div class = "line" > < a id = "l00818" name = "l00818" > < / a > < span class = "lineno" > 818< / span > < span class = "comment" > // I suspect I am not the first one to need something similar.< / span > < / div >
< div class = "line" > < a id = "l00819" name = "l00819" > < / a > < span class = "lineno" > 819< / span > < span class = "keyword" > mutable< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > DenseBooleanColumn< / a > marked_;< / div >
< div class = "line" > < a id = "l00820" name = "l00820" > < / a > < span class = "lineno" > 820< / span > < / div >
< div class = "line" > < a id = "l00821" name = "l00821" > < / a > < span class = "lineno" > 821< / span > < span class = "comment" > // This is used to represent a pruned sub-matrix of the current matrix that< / span > < / div >
< div class = "line" > < a id = "l00822" name = "l00822" > < / a > < span class = "lineno" > 822< / span > < span class = "comment" > // corresponds to the pruned DAG as described in the comment above for< / span > < / div >
< div class = "line" > < a id = "l00823" name = "l00823" > < / a > < span class = "lineno" > 823< / span > < span class = "comment" > // marked_. This vector is used to encode the sub-matrix as follow:< / span > < / div >
< div class = "line" > < a id = "l00824" name = "l00824" > < / a > < span class = "lineno" > 824< / span > < span class = "comment" > // - Both the rows and the coefficients of the pruned matrix are still stored< / span > < / div >
< div class = "line" > < a id = "l00825" name = "l00825" > < / a > < span class = "lineno" > 825< / span > < span class = "comment" > // in rows_ and coefficients_.< / span > < / div >
< div class = "line" > < a id = "l00826" name = "l00826" > < / a > < span class = "lineno" > 826< / span > < span class = "comment" > // - The data of column ' col' is still stored starting at starts_[col].< / span > < / div >
< div class = "line" > < a id = "l00827" name = "l00827" > < / a > < span class = "lineno" > 827< / span > < span class = "comment" > // - But, its end is given by pruned_ends_[col] instead of starts_[col + 1].< / span > < / div >
< div class = "line" > < a id = "l00828" name = "l00828" > < / a > < span class = "lineno" > 828< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00829" name = "l00829" > < / a > < span class = "lineno" > 829< / span > < span class = "comment" > // The idea of using a smaller graph for the symbolic phase is well known in< / span > < / div >
< div class = "line" > < a id = "l00830" name = "l00830" > < / a > < span class = "lineno" > 830< / span > < span class = "comment" > // sparse linear algebra. See:< / span > < / div >
< div class = "line" > < a id = "l00831" name = "l00831" > < / a > < span class = "lineno" > 831< / span > < span class = "comment" > // - John R. Gilbert and Joseph W. H. Liu, " Elimination structures for< / span > < / div >
< div class = "line" > < a id = "l00832" name = "l00832" > < / a > < span class = "lineno" > 832< / span > < span class = "comment" > // unsymmetric sparse LU factors" , Tech. Report CS-90-11. Departement of< / span > < / div >
< div class = "line" > < a id = "l00833" name = "l00833" > < / a > < span class = "lineno" > 833< / span > < span class = "comment" > // Computer Science, York University, North York. Ontario, Canada, 1990.< / span > < / div >
< div class = "line" > < a id = "l00834" name = "l00834" > < / a > < span class = "lineno" > 834< / span > < span class = "comment" > // - Stanley C. Eisenstat and Joseph W. H. Liu, " Exploiting structural< / span > < / div >
< div class = "line" > < a id = "l00835" name = "l00835" > < / a > < span class = "lineno" > 835< / span > < span class = "comment" > // symmetry in a sparse partial pivoting code" . SIAM J. Sci. Comput. Vol< / span > < / div >
< div class = "line" > < a id = "l00836" name = "l00836" > < / a > < span class = "lineno" > 836< / span > < span class = "comment" > // 14, No 1, pp. 253-257, January 1993.< / span > < / div >
< div class = "line" > < a id = "l00837" name = "l00837" > < / a > < span class = "lineno" > 837< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00838" name = "l00838" > < / a > < span class = "lineno" > 838< / span > < span class = "comment" > // Note that we use an original algorithm and prune the graph while performing< / span > < / div >
< div class = "line" > < a id = "l00839" name = "l00839" > < / a > < span class = "lineno" > 839< / span > < span class = "comment" > // the symbolic phase. Hence the pruning will only benefit the next symbolic< / span > < / div >
< div class = "line" > < a id = "l00840" name = "l00840" > < / a > < span class = "lineno" > 840< / span > < span class = "comment" > // phase. This is different from Eisenstat-Liu' s symmetric pruning. It is< / span > < / div >
< div class = "line" > < a id = "l00841" name = "l00841" > < / a > < span class = "lineno" > 841< / span > < span class = "comment" > // still a heuristic and will not necessarily find the minimal graph that< / span > < / div >
< div class = "line" > < a id = "l00842" name = "l00842" > < / a > < span class = "lineno" > 842< / span > < span class = "comment" > // has the same result for the symbolic phase though.< / span > < / div >
< div class = "line" > < a id = "l00843" name = "l00843" > < / a > < span class = "lineno" > 843< / span > < span class = "comment" > //< / span > < / div >
< div class = "line" > < a id = "l00844" name = "l00844" > < / a > < span class = "lineno" > 844< / span > < span class = "comment" > // TODO(user): Use this during the " normal" hyper-sparse solves so that< / span > < / div >
< div class = "line" > < a id = "l00845" name = "l00845" > < / a > < span class = "lineno" > 845< / span > < span class = "comment" > // we can benefit from the pruned lower matrix there?< / span > < / div >
< div class = "line" > < a id = "l00846" name = "l00846" > < / a > < span class = "lineno" > 846< / span > < a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > StrictITIVector< ColIndex, EntryIndex> < / a > pruned_ends_;< / div >
< div class = "line" > < a id = "l00847" name = "l00847" > < / a > < span class = "lineno" > 847< / span > < / div >
< div class = "line" > < a id = "l00848" name = "l00848" > < / a > < span class = "lineno" > 848< / span > < a class = "code hl_define" href = "macros_8h.html#af8df3547bfde53a5acb93e2607b0034a" > DISALLOW_COPY_AND_ASSIGN< / a > (< a class = "code hl_class" href = "classoperations__research_1_1glop_1_1_triangular_matrix.html" > TriangularMatrix< / a > );< / div >
< div class = "line" > < a id = "l00849" name = "l00849" > < / a > < span class = "lineno" > 849< / span > };< / div >
< div class = "line" > < a id = "l00850" name = "l00850" > < / a > < span class = "lineno" > 850< / span > < / div >
< div class = "line" > < a id = "l00851" name = "l00851" > < / a > < span class = "lineno" > 851< / span > } < span class = "comment" > // namespace glop< / span > < / div >
< div class = "line" > < a id = "l00852" name = "l00852" > < / a > < span class = "lineno" > 852< / span > } < span class = "comment" > // namespace operations_research< / span > < / div >
< div class = "line" > < a id = "l00853" name = "l00853" > < / a > < span class = "lineno" > 853< / span > < / div >
< div class = "line" > < a id = "l00854" name = "l00854" > < / a > < span class = "lineno" > 854< / span > < span class = "preprocessor" > #endif < / span > < span class = "comment" > // OR_TOOLS_LP_DATA_SPARSE_H_< / span > < / div >
< div class = "ttc" id = "aalldiff__cst_8cc_html_a26e6db9bcc64b584051ecc28171ed11f" > < div class = "ttname" > < a href = "alldiff__cst_8cc.html#a26e6db9bcc64b584051ecc28171ed11f" > max< / a > < / div > < div class = "ttdeci" > int64_t max< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "alldiff__cst_8cc_source.html#l00140" > alldiff_cst.cc:140< / a > < / div > < / div >
2021-12-14 13:41:01 +01:00
< div class = "ttc" id = "abase_2logging_8h_html_ab62f5ed8f2d48e29802be0cbbcd1359a" > < div class = "ttname" > < a href = "base_2logging_8h.html#ab62f5ed8f2d48e29802be0cbbcd1359a" > DCHECK_LT< / a > < / div > < div class = "ttdeci" > #define DCHECK_LT(val1, val2)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "base_2logging_8h_source.html#l00890" > base/logging.h:890< / a > < / div > < / div >
< div class = "ttc" id = "abase_2logging_8h_html_ae89df3243bbb8341130c7b3f44145ea0" > < div class = "ttname" > < a href = "base_2logging_8h.html#ae89df3243bbb8341130c7b3f44145ea0" > DCHECK_EQ< / a > < / div > < div class = "ttdeci" > #define DCHECK_EQ(val1, val2)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "base_2logging_8h_source.html#l00887" > base/logging.h:887< / a > < / div > < / div >
2021-09-30 01:18:45 +02:00
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_column_view_html" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_column_view.html" > operations_research::glop::ColumnView< / a > < / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse__column_8h_source.html#l00065" > sparse_column.h:65< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_html" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html" > operations_research::glop::CompactSparseMatrix< / a > < / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00289" > sparse.h:289< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_html_a0358eb2d6ea480b59d89dc42326cf840" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a0358eb2d6ea480b59d89dc42326cf840" > operations_research::glop::CompactSparseMatrix::num_cols_< / a > < / div > < div class = "ttdeci" > ColIndex num_cols_< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00459" > sparse.h:459< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_html_a1426d8ab983ec32193c571f5e8c02cda" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a1426d8ab983ec32193c571f5e8c02cda" > operations_research::glop::CompactSparseMatrix::ColumnIsEmpty< / a > < / div > < div class = "ttdeci" > bool ColumnIsEmpty(ColIndex col) const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00381" > sparse.h:381< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_html_a28058c5e9ff6638ea1ea210b49a4e7bc" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a28058c5e9ff6638ea1ea210b49a4e7bc" > operations_research::glop::CompactSparseMatrix::ColumnCopyToDenseColumn< / a > < / div > < div class = "ttdeci" > void ColumnCopyToDenseColumn(ColIndex col, DenseColumn *dense_column) const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00423" > sparse.h:423< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_html_a319ffa92d03907ee98b5f3da18421af3" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a319ffa92d03907ee98b5f3da18421af3" > operations_research::glop::CompactSparseMatrix::CompactSparseMatrix< / a > < / div > < div class = "ttdeci" > CompactSparseMatrix()< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00291" > sparse.h:291< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_html_a37ac057f213297550a26947d551324a3" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a37ac057f213297550a26947d551324a3" > operations_research::glop::CompactSparseMatrix::starts_< / a > < / div > < div class = "ttdeci" > StrictITIVector< ColIndex, EntryIndex > starts_< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00466" > sparse.h:466< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_html_a41741829541d089f1c4d34f190884813" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a41741829541d089f1c4d34f190884813" > operations_research::glop::CompactSparseMatrix::num_cols< / a > < / div > < div class = "ttdeci" > ColIndex num_cols() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00350" > sparse.h:350< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_html_a5d6d5d7a7944b09bd0df4b7132fe5f7e" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a5d6d5d7a7944b09bd0df4b7132fe5f7e" > operations_research::glop::CompactSparseMatrix::num_rows_< / a > < / div > < div class = "ttdeci" > RowIndex num_rows_< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00458" > sparse.h:458< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_html_a6e49e4127a33039fcccc6e50380faefa" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a6e49e4127a33039fcccc6e50380faefa" > operations_research::glop::CompactSparseMatrix::ColumnAddMultipleToSparseScatteredColumn< / a > < / div > < div class = "ttdeci" > void ColumnAddMultipleToSparseScatteredColumn(ColIndex col, Fractional multiplier, ScatteredColumn *column) const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00410" > sparse.h:410< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_html_a8a0e8a1a3afc70e2678d046feb11d024" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a8a0e8a1a3afc70e2678d046feb11d024" > operations_research::glop::CompactSparseMatrix::ColumnCopyToClearedDenseColumnWithNonZeros< / a > < / div > < div class = "ttdeci" > void ColumnCopyToClearedDenseColumnWithNonZeros(ColIndex col, DenseColumn *dense_column, RowIndexVector *non_zeros) const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00441" > sparse.h:441< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_html_a8da36920b149053499a21e50fc859a93" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a8da36920b149053499a21e50fc859a93" > operations_research::glop::CompactSparseMatrix::rows_< / a > < / div > < div class = "ttdeci" > StrictITIVector< EntryIndex, RowIndex > rows_< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00465" > sparse.h:465< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_html_a8e12342fc420701fbffd97025421575a" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a8e12342fc420701fbffd97025421575a" > operations_research::glop::CompactSparseMatrix::IsEmpty< / a > < / div > < div class = "ttdeci" > bool IsEmpty() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00353" > sparse.h:353< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_html_a960110e64357a3e69162ebf1f71959dd" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a960110e64357a3e69162ebf1f71959dd" > operations_research::glop::CompactSparseMatrix::num_rows< / a > < / div > < div class = "ttdeci" > RowIndex num_rows() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00349" > sparse.h:349< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_html_a9f271f559e0d1e794a2ecc76d919db68" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#a9f271f559e0d1e794a2ecc76d919db68" > operations_research::glop::CompactSparseMatrix::CompactSparseMatrix< / a > < / div > < div class = "ttdeci" > CompactSparseMatrix(const SparseMatrix & matrix)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00295" > sparse.h:295< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_html_aaef7fc778a29bb3bb3040c0423937f6e" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#aaef7fc778a29bb3bb3040c0423937f6e" > operations_research::glop::CompactSparseMatrix::EntryCoefficient< / a > < / div > < div class = "ttdeci" > Fractional EntryCoefficient(EntryIndex i) const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00366" > sparse.h:366< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_html_ab392807d136adb480aedec7750cbbb18" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#ab392807d136adb480aedec7750cbbb18" > operations_research::glop::CompactSparseMatrix::coefficients_< / a > < / div > < div class = "ttdeci" > StrictITIVector< EntryIndex, Fractional > coefficients_< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00464" > sparse.h:464< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_html_ab9bd1cef3f6a18704cb7d9ce6201e106" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#ab9bd1cef3f6a18704cb7d9ce6201e106" > operations_research::glop::CompactSparseMatrix::ColumnCopyToClearedDenseColumn< / a > < / div > < div class = "ttdeci" > void ColumnCopyToClearedDenseColumn(ColIndex col, DenseColumn *dense_column) const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00431" > sparse.h:431< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_html_abdd940ad64b555052b33e763b80aea26" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#abdd940ad64b555052b33e763b80aea26" > operations_research::glop::CompactSparseMatrix::ColumnScalarProduct< / a > < / div > < div class = "ttdeci" > Fractional ColumnScalarProduct(ColIndex col, const DenseRow & vector) const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00387" > sparse.h:387< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_html_acedaf830dd26be6213e4665f088c5aa4" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#acedaf830dd26be6213e4665f088c5aa4" > operations_research::glop::CompactSparseMatrix::Column< / a > < / div > < div class = "ttdeci" > ::util::IntegerRange< EntryIndex > Column(ColIndex col) const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00363" > sparse.h:363< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_html_aea0e9a84b41c95c874f171cae97cf31b" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#aea0e9a84b41c95c874f171cae97cf31b" > operations_research::glop::CompactSparseMatrix::ColumnAddMultipleToDenseColumn< / a > < / div > < div class = "ttdeci" > void ColumnAddMultipleToDenseColumn(ColIndex col, Fractional multiplier, DenseColumn *dense_column) const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00398" > sparse.h:398< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_html_aedc46de5199e203b77de2eae2e4c100d" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#aedc46de5199e203b77de2eae2e4c100d" > operations_research::glop::CompactSparseMatrix::EntryRow< / a > < / div > < div class = "ttdeci" > RowIndex EntryRow(EntryIndex i) const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00367" > sparse.h:367< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_html_af69d9b7065a8f31604a8134be4307749" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#af69d9b7065a8f31604a8134be4307749" > operations_research::glop::CompactSparseMatrix::num_entries< / a > < / div > < div class = "ttdeci" > EntryIndex num_entries() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00345" > sparse.h:345< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_html_afbe7c81d6b4066bf7874299a0f7c0d59" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#afbe7c81d6b4066bf7874299a0f7c0d59" > operations_research::glop::CompactSparseMatrix::column< / a > < / div > < div class = "ttdeci" > ColumnView column(ColIndex col) const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00369" > sparse.h:369< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_html_afe3d36f3ba4f04442fbb36f8726f8baf" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix.html#afe3d36f3ba4f04442fbb36f8726f8baf" > operations_research::glop::CompactSparseMatrix::ColumnNumEntries< / a > < / div > < div class = "ttdeci" > EntryIndex ColumnNumEntries(ColIndex col) const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00340" > sparse.h:340< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_view_html" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix_view.html" > operations_research::glop::CompactSparseMatrixView< / a > < / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00476" > sparse.h:476< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_view_html_a41741829541d089f1c4d34f190884813" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix_view.html#a41741829541d089f1c4d34f190884813" > operations_research::glop::CompactSparseMatrixView::num_cols< / a > < / div > < div class = "ttdeci" > ColIndex num_cols() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00489" > sparse.h:489< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_view_html_a6c2cb025b83ee5d5365eb0b419a0298c" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix_view.html#a6c2cb025b83ee5d5365eb0b419a0298c" > operations_research::glop::CompactSparseMatrixView::column< / a > < / div > < div class = "ttdeci" > const ColumnView column(ColIndex col) const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00490" > sparse.h:490< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_view_html_a6e483ed6906f126dc6fa63d198c8f907" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix_view.html#a6e483ed6906f126dc6fa63d198c8f907" > operations_research::glop::CompactSparseMatrixView::CompactSparseMatrixView< / a > < / div > < div class = "ttdeci" > CompactSparseMatrixView(const CompactSparseMatrix *compact_matrix, const RowToColMapping *basis)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00478" > sparse.h:478< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_view_html_a7a56df5528de85e8d1b588d6a50e6948" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix_view.html#a7a56df5528de85e8d1b588d6a50e6948" > operations_research::glop::CompactSparseMatrixView::CompactSparseMatrixView< / a > < / div > < div class = "ttdeci" > CompactSparseMatrixView(const CompactSparseMatrix *compact_matrix, const std::vector< ColIndex > *columns)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00482" > sparse.h:482< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_view_html_a8e12342fc420701fbffd97025421575a" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix_view.html#a8e12342fc420701fbffd97025421575a" > operations_research::glop::CompactSparseMatrixView::IsEmpty< / a > < / div > < div class = "ttdeci" > bool IsEmpty() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00487" > sparse.h:487< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_compact_sparse_matrix_view_html_a960110e64357a3e69162ebf1f71959dd" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_compact_sparse_matrix_view.html#a960110e64357a3e69162ebf1f71959dd" > operations_research::glop::CompactSparseMatrixView::num_rows< / a > < / div > < div class = "ttdeci" > RowIndex num_rows() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00488" > sparse.h:488< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_matrix_view_html" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_matrix_view.html" > operations_research::glop::MatrixView< / a > < / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00219" > sparse.h:219< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_matrix_view_html_a1f0797ca04f7cb50938328e7e027a18b" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_matrix_view.html#a1f0797ca04f7cb50938328e7e027a18b" > operations_research::glop::MatrixView::MatrixView< / a > < / div > < div class = "ttdeci" > MatrixView()< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00221" > sparse.h:221< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_matrix_view_html_a3f219a081f88c22ae282ada4f0bdddd3" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_matrix_view.html#a3f219a081f88c22ae282ada4f0bdddd3" > operations_research::glop::MatrixView::ComputeInfinityNorm< / a > < / div > < div class = "ttdeci" > Fractional ComputeInfinityNorm() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00423" > sparse.cc:423< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_matrix_view_html_a41741829541d089f1c4d34f190884813" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_matrix_view.html#a41741829541d089f1c4d34f190884813" > operations_research::glop::MatrixView::num_cols< / a > < / div > < div class = "ttdeci" > ColIndex num_cols() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00264" > sparse.h:264< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_matrix_view_html_a495dfea7028bd3b07c1485d5c66b7001" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_matrix_view.html#a495dfea7028bd3b07c1485d5c66b7001" > operations_research::glop::MatrixView::PopulateFromMatrix< / a > < / div > < div class = "ttdeci" > void PopulateFromMatrix(const SparseMatrix & matrix)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00227" > sparse.h:227< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_matrix_view_html_a64fea3282d498f3eb2d4af70692bb117" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_matrix_view.html#a64fea3282d498f3eb2d4af70692bb117" > operations_research::glop::MatrixView::ComputeOneNorm< / a > < / div > < div class = "ttdeci" > Fractional ComputeOneNorm() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00420" > sparse.cc:420< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_matrix_view_html_a7273cc492a51a1c5d45c620b32fce502" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_matrix_view.html#a7273cc492a51a1c5d45c620b32fce502" > operations_research::glop::MatrixView::column< / a > < / div > < div class = "ttdeci" > const SparseColumn & column(ColIndex col) const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00265" > sparse.h:265< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_matrix_view_html_a87c606b7a9b920de2d4b6aa5c3bc1a45" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_matrix_view.html#a87c606b7a9b920de2d4b6aa5c3bc1a45" > operations_research::glop::MatrixView::PopulateFromMatrixPair< / a > < / div > < div class = "ttdeci" > void PopulateFromMatrixPair(const SparseMatrix & matrix_a, const SparseMatrix & matrix_b)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00238" > sparse.h:238< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_matrix_view_html_a8e12342fc420701fbffd97025421575a" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_matrix_view.html#a8e12342fc420701fbffd97025421575a" > operations_research::glop::MatrixView::IsEmpty< / a > < / div > < div class = "ttdeci" > bool IsEmpty() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00262" > sparse.h:262< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_matrix_view_html_a960110e64357a3e69162ebf1f71959dd" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_matrix_view.html#a960110e64357a3e69162ebf1f71959dd" > operations_research::glop::MatrixView::num_rows< / a > < / div > < div class = "ttdeci" > RowIndex num_rows() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00263" > sparse.h:263< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_matrix_view_html_ac220f9bed6efccb65c2514e90d702638" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_matrix_view.html#ac220f9bed6efccb65c2514e90d702638" > operations_research::glop::MatrixView::MatrixView< / a > < / div > < div class = "ttdeci" > MatrixView(const SparseMatrix & matrix)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00222" > sparse.h:222< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_matrix_view_html_adc9aa1d344fe9442ac3ba673b939db7c" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_matrix_view.html#adc9aa1d344fe9442ac3ba673b939db7c" > operations_research::glop::MatrixView::PopulateFromBasis< / a > < / div > < div class = "ttdeci" > void PopulateFromBasis(const MatrixView & matrix, const RowToColMapping & basis)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00252" > sparse.h:252< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_matrix_view_html_af69d9b7065a8f31604a8134be4307749" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_matrix_view.html#af69d9b7065a8f31604a8134be4307749" > operations_research::glop::MatrixView::num_entries< / a > < / div > < div class = "ttdeci" > EntryIndex num_entries() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00419" > sparse.cc:419< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_permutation_html" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_permutation.html" > operations_research::glop::Permutation< RowIndex > < / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_column_html" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_column.html" > operations_research::glop::SparseColumn< / a > < / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse__column_8h_source.html#l00044" > sparse_column.h:44< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html" > operations_research::glop::SparseMatrix< / a > < / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00062" > sparse.h:62< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_a06d22c92f8b45b18560b46797f98a81b" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a06d22c92f8b45b18560b46797f98a81b" > operations_research::glop::SparseMatrix::AppendUnitVector< / a > < / div > < div class = "ttdeci" > void AppendUnitVector(RowIndex row, Fractional value)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00151" > sparse.cc:151< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_a0c406d94c3586159071e0b370a5a02fb" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a0c406d94c3586159071e0b370a5a02fb" > operations_research::glop::SparseMatrix::PopulateFromLinearCombination< / a > < / div > < div class = "ttdeci" > void PopulateFromLinearCombination(Fractional alpha, const SparseMatrix & a, Fractional beta, const SparseMatrix & b)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00225" > sparse.cc:225< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_a29edb960f882c54b9652853e94988e79" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a29edb960f882c54b9652853e94988e79" > operations_research::glop::SparseMatrix::PopulateFromPermutedMatrix< / a > < / div > < div class = "ttdeci" > void PopulateFromPermutedMatrix(const Matrix & a, const RowPermutation & row_perm, const ColumnPermutation & inverse_col_perm)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00212" > sparse.cc:212< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_a2e5611d47d02e1029b98a8e9bee3469f" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a2e5611d47d02e1029b98a8e9bee3469f" > operations_research::glop::SparseMatrix::CheckNoDuplicates< / a > < / div > < div class = "ttdeci" > bool CheckNoDuplicates() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00126" > sparse.cc:126< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_a2fe6f7470512f5301031480737375c88" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a2fe6f7470512f5301031480737375c88" > operations_research::glop::SparseMatrix::PopulateFromTranspose< / a > < / div > < div class = "ttdeci" > void PopulateFromTranspose(const Matrix & input)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00181" > sparse.cc:181< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_a323801972c3d6de340e260de4582c34b" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a323801972c3d6de340e260de4582c34b" > operations_research::glop::SparseMatrix::PopulateFromIdentity< / a > < / div > < div class = "ttdeci" > void PopulateFromIdentity(ColIndex num_cols)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00172" > sparse.cc:172< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_a3f219a081f88c22ae282ada4f0bdddd3" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a3f219a081f88c22ae282ada4f0bdddd3" > operations_research::glop::SparseMatrix::ComputeInfinityNorm< / a > < / div > < div class = "ttdeci" > Fractional ComputeInfinityNorm() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00395" > sparse.cc:395< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_a41741829541d089f1c4d34f190884813" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a41741829541d089f1c4d34f190884813" > operations_research::glop::SparseMatrix::num_cols< / a > < / div > < div class = "ttdeci" > ColIndex num_cols() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00178" > sparse.h:178< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_a55265e26d9e69e2d7a882bab054b8139" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a55265e26d9e69e2d7a882bab054b8139" > operations_research::glop::SparseMatrix::SetNumRows< / a > < / div > < div class = "ttdeci" > void SetNumRows(RowIndex num_rows)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00143" > sparse.cc:143< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_a564caf35589006190ef4985fbda74faa" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a564caf35589006190ef4985fbda74faa" > operations_research::glop::SparseMatrix::mutable_column< / a > < / div > < div class = "ttdeci" > SparseColumn * mutable_column(ColIndex col)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00182" > sparse.h:182< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_a566008ab9fd3e3dbec96263bc3c45061" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a566008ab9fd3e3dbec96263bc3c45061" > operations_research::glop::SparseMatrix::LookUpValue< / a > < / div > < div class = "ttdeci" > Fractional LookUpValue(RowIndex row, ColIndex col) const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00323" > sparse.cc:323< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_a5e016d204d43b2cc4a2773c25462968a" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a5e016d204d43b2cc4a2773c25462968a" > operations_research::glop::SparseMatrix::IsCleanedUp< / a > < / div > < div class = "ttdeci" > bool IsCleanedUp() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00135" > sparse.cc:135< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_a6052657cbffbe19decf328bf369d58e1" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a6052657cbffbe19decf328bf369d58e1" > operations_research::glop::SparseMatrix::Swap< / a > < / div > < div class = "ttdeci" > void Swap(SparseMatrix *matrix)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00158" > sparse.cc:158< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_a64fea3282d498f3eb2d4af70692bb117" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a64fea3282d498f3eb2d4af70692bb117" > operations_research::glop::SparseMatrix::ComputeOneNorm< / a > < / div > < div class = "ttdeci" > Fractional ComputeOneNorm() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00392" > sparse.cc:392< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_a6ae7b0836055b9b6d182115027d496f9" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a6ae7b0836055b9b6d182115027d496f9" > operations_research::glop::SparseMatrix::ComputeMinAndMaxMagnitudes< / a > < / div > < div class = "ttdeci" > void ComputeMinAndMaxMagnitudes(Fractional *min_magnitude, Fractional *max_magnitude) const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00369" > sparse.cc:369< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_a7273cc492a51a1c5d45c620b32fce502" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a7273cc492a51a1c5d45c620b32fce502" > operations_research::glop::SparseMatrix::column< / a > < / div > < div class = "ttdeci" > const SparseColumn & column(ColIndex col) const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00181" > sparse.h:181< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_a79446a803c1bed8b17c8ac937d07be39" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a79446a803c1bed8b17c8ac937d07be39" > operations_research::glop::SparseMatrix::SparseMatrix< / a > < / div > < div class = "ttdeci" > SparseMatrix()< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00087" > sparse.cc:87< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_a860e7a37e8c7c2f5f7f0a73d3e3473f0" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a860e7a37e8c7c2f5f7f0a73d3e3473f0" > operations_research::glop::SparseMatrix::DeleteRows< / a > < / div > < div class = "ttdeci" > void DeleteRows(RowIndex num_rows, const RowPermutation & permutation)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00289" > sparse.cc:289< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_a86fd105be79f4f2dbaf3a21e64e4d022" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a86fd105be79f4f2dbaf3a21e64e4d022" > operations_research::glop::SparseMatrix::AppendEmptyColumn< / a > < / div > < div class = "ttdeci" > ColIndex AppendEmptyColumn()< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00145" > sparse.cc:145< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_a8e12342fc420701fbffd97025421575a" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a8e12342fc420701fbffd97025421575a" > operations_research::glop::SparseMatrix::IsEmpty< / a > < / div > < div class = "ttdeci" > bool IsEmpty() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00115" > sparse.cc:115< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_a960110e64357a3e69162ebf1f71959dd" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#a960110e64357a3e69162ebf1f71959dd" > operations_research::glop::SparseMatrix::num_rows< / a > < / div > < div class = "ttdeci" > RowIndex num_rows() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00177" > sparse.h:177< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_aa1c2872fa7d491d4c093c8b2124a53b9" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#aa1c2872fa7d491d4c093c8b2124a53b9" > operations_research::glop::SparseMatrix::PopulateFromProduct< / a > < / div > < div class = "ttdeci" > void PopulateFromProduct(const SparseMatrix & a, const SparseMatrix & b)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00250" > sparse.cc:250< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_aa71d36872f416feaa853788a7a7a7ef8" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#aa71d36872f416feaa853788a7a7a7ef8" > operations_research::glop::SparseMatrix::Clear< / a > < / div > < div class = "ttdeci" > void Clear()< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00110" > sparse.cc:110< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_ab2d8beb101c26b08a8af602300da1748" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#ab2d8beb101c26b08a8af602300da1748" > operations_research::glop::SparseMatrix::AppendRowsFromSparseMatrix< / a > < / div > < div class = "ttdeci" > bool AppendRowsFromSparseMatrix(const SparseMatrix & matrix)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00302" > sparse.cc:302< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_ab4a8456683d4572bd9426efab8489e99" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#ab4a8456683d4572bd9426efab8489e99" > operations_research::glop::SparseMatrix::Dump< / a > < / div > < div class = "ttdeci" > std::string Dump() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00399" > sparse.cc:399< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_abfc30f91ab75c6f4552003f777672e74" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#abfc30f91ab75c6f4552003f777672e74" > operations_research::glop::SparseMatrix::CleanUp< / a > < / div > < div class = "ttdeci" > void CleanUp()< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00119" > sparse.cc:119< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_ac59fd9cddfe284bf9dc7581ed631ce8d" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#ac59fd9cddfe284bf9dc7581ed631ce8d" > operations_research::glop::SparseMatrix::DeleteColumns< / a > < / div > < div class = "ttdeci" > void DeleteColumns(const DenseBooleanRow & columns_to_delete)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00276" > sparse.cc:276< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_acbbc88405e6db3fe77064e1a3d4e402a" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#acbbc88405e6db3fe77064e1a3d4e402a" > operations_research::glop::SparseMatrix::PopulateFromSparseMatrix< / a > < / div > < div class = "ttdeci" > void PopulateFromSparseMatrix(const SparseMatrix & matrix)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00206" > sparse.cc:206< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_ace1973900f4d921a7bedbcbe36e1bcad" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#ace1973900f4d921a7bedbcbe36e1bcad" > operations_research::glop::SparseMatrix::ApplyRowPermutation< / a > < / div > < div class = "ttdeci" > void ApplyRowPermutation(const RowPermutation & row_perm)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00316" > sparse.cc:316< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_ae1b90982a83e1b025ebbc1c446980640" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#ae1b90982a83e1b025ebbc1c446980640" > operations_research::glop::SparseMatrix::PopulateFromZero< / a > < / div > < div class = "ttdeci" > void PopulateFromZero(RowIndex num_rows, ColIndex num_cols)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00164" > sparse.cc:164< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_af69d9b7065a8f31604a8134be4307749" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#af69d9b7065a8f31604a8134be4307749" > operations_research::glop::SparseMatrix::num_entries< / a > < / div > < div class = "ttdeci" > EntryIndex num_entries() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00389" > sparse.cc:389< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_sparse_matrix_html_af782744bdb01cf56841a0de18ccca3ce" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_sparse_matrix.html#af782744bdb01cf56841a0de18ccca3ce" > operations_research::glop::SparseMatrix::Equals< / a > < / div > < div class = "ttdeci" > bool Equals(const SparseMatrix & a, Fractional tolerance) const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8cc_source.html#l00327" > sparse.cc:327< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_strict_i_t_i_vector_html" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html" > operations_research::glop::StrictITIVector< ColIndex, bool > < / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_strict_i_t_i_vector_html_a3de922485ca2c30f3e07d959dd97cdd0" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html#a3de922485ca2c30f3e07d959dd97cdd0" > operations_research::glop::StrictITIVector::AssignToZero< / a > < / div > < div class = "ttdeci" > void AssignToZero(IntType size)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "lp__types_8h_source.html#l00294" > lp_types.h:294< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_strict_i_t_i_vector_html_a64b6b04f3a519d2c61d49daaa88bf06e" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html#a64b6b04f3a519d2c61d49daaa88bf06e" > operations_research::glop::StrictITIVector::resize< / a > < / div > < div class = "ttdeci" > void resize(IntType size)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "lp__types_8h_source.html#l00273" > lp_types.h:273< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_strict_i_t_i_vector_html_a967a5c081ad4195a30c78dc2c0bcabf5" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_strict_i_t_i_vector.html#a967a5c081ad4195a30c78dc2c0bcabf5" > operations_research::glop::StrictITIVector::size< / a > < / div > < div class = "ttdeci" > IntType size() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "lp__types_8h_source.html#l00280" > lp_types.h:280< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_triangular_matrix_html" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_triangular_matrix.html" > operations_research::glop::TriangularMatrix< / a > < / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00511" > sparse.h:511< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_triangular_matrix_html_a3d3abc3e6b522dc60fd8c1168522e09d" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#a3d3abc3e6b522dc60fd8c1168522e09d" > operations_research::glop::TriangularMatrix::GetFirstNonIdentityColumn< / a > < / div > < div class = "ttdeci" > ColIndex GetFirstNonIdentityColumn() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00576" > sparse.h:576< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_triangular_matrix_html_a41741829541d089f1c4d34f190884813" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#a41741829541d089f1c4d34f190884813" > operations_research::glop::TriangularMatrix::num_cols< / a > < / div > < div class = "ttdeci" > ColIndex num_cols() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00522" > sparse.h:522< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_triangular_matrix_html_a4a34504e7ba1673cf24f745d162fda84" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#a4a34504e7ba1673cf24f745d162fda84" > operations_research::glop::TriangularMatrix::GetDiagonalCoefficient< / a > < / div > < div class = "ttdeci" > Fractional GetDiagonalCoefficient(ColIndex col) const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00581" > sparse.h:581< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_triangular_matrix_html_a8e12342fc420701fbffd97025421575a" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#a8e12342fc420701fbffd97025421575a" > operations_research::glop::TriangularMatrix::IsEmpty< / a > < / div > < div class = "ttdeci" > bool IsEmpty() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00520" > sparse.h:520< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_triangular_matrix_html_a960110e64357a3e69162ebf1f71959dd" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#a960110e64357a3e69162ebf1f71959dd" > operations_research::glop::TriangularMatrix::num_rows< / a > < / div > < div class = "ttdeci" > RowIndex num_rows() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00521" > sparse.h:521< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_triangular_matrix_html_ab8101094fb842f9cb500b3dfadc325d3" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#ab8101094fb842f9cb500b3dfadc325d3" > operations_research::glop::TriangularMatrix::TriangularMatrix< / a > < / div > < div class = "ttdeci" > TriangularMatrix()< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00513" > sparse.h:513< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_triangular_matrix_html_aec8942e8b01f9aed2abc24de9acfb6ab" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#aec8942e8b01f9aed2abc24de9acfb6ab" > operations_research::glop::TriangularMatrix::NumFpOperationsInLastPermutedLowerSparseSolve< / a > < / div > < div class = "ttdeci" > int64_t NumFpOperationsInLastPermutedLowerSparseSolve() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00709" > sparse.h:709< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_triangular_matrix_html_af0dafb025bcf4174501a93fb91ca4bb6" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#af0dafb025bcf4174501a93fb91ca4bb6" > operations_research::glop::TriangularMatrix::ColumnIsDiagonalOnly< / a > < / div > < div class = "ttdeci" > bool ColumnIsDiagonalOnly(ColIndex col) const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00586" > sparse.h:586< / a > < / div > < / div >
< div class = "ttc" id = "aclassoperations__research_1_1glop_1_1_triangular_matrix_html_af69d9b7065a8f31604a8134be4307749" > < div class = "ttname" > < a href = "classoperations__research_1_1glop_1_1_triangular_matrix.html#af69d9b7065a8f31604a8134be4307749" > operations_research::glop::TriangularMatrix::num_entries< / a > < / div > < div class = "ttdeci" > EntryIndex num_entries() const< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sparse_8h_source.html#l00523" > sparse.h:523< / a > < / div > < / div >
< div class = "ttc" id = "aclassutil_1_1_integer_range_html" > < div class = "ttname" > < a href = "classutil_1_1_integer_range.html" > util::IntegerRange< / a > < / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "iterators_8h_source.html#l00146" > iterators.h:146< / a > < / div > < / div >
< div class = "ttc" id = "aconstraint__solver_2table_8cc_html_a9293e4d29cac928301645070dd307683" > < div class = "ttname" > < a href = "constraint__solver_2table_8cc.html#a9293e4d29cac928301645070dd307683" > b< / a > < / div > < div class = "ttdeci" > int64_t b< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "constraint__solver_2table_8cc_source.html#l00047" > constraint_solver/table.cc:47< / a > < / div > < / div >
< div class = "ttc" id = "aconstraint__solver_2table_8cc_html_acb18315d548212835cd8ed4287e6c0b6" > < div class = "ttname" > < a href = "constraint__solver_2table_8cc.html#acb18315d548212835cd8ed4287e6c0b6" > a< / a > < / div > < div class = "ttdeci" > int64_t a< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "constraint__solver_2table_8cc_source.html#l00046" > constraint_solver/table.cc:46< / a > < / div > < / div >
< div class = "ttc" id = "ademon__profiler_8cc_html_ac072af30c4ffbc834bb4c681f6ecb514" > < div class = "ttname" > < a href = "demon__profiler_8cc.html#ac072af30c4ffbc834bb4c681f6ecb514" > value< / a > < / div > < div class = "ttdeci" > int64_t value< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "demon__profiler_8cc_source.html#l00044" > demon_profiler.cc:44< / a > < / div > < / div >
< div class = "ttc" id = "aintegral__types_8h_html" > < div class = "ttname" > < a href = "integral__types_8h.html" > integral_types.h< / a > < / div > < / div >
< div class = "ttc" id = "alp__data_2permutation_8h_html" > < div class = "ttname" > < a href = "lp__data_2permutation_8h.html" > permutation.h< / a > < / div > < / div >
< div class = "ttc" id = "alp__types_8h_html" > < div class = "ttname" > < a href = "lp__types_8h.html" > lp_types.h< / a > < / div > < / div >
< div class = "ttc" id = "amacros_8h_html_af8df3547bfde53a5acb93e2607b0034a" > < div class = "ttname" > < a href = "macros_8h.html#af8df3547bfde53a5acb93e2607b0034a" > DISALLOW_COPY_AND_ASSIGN< / a > < / div > < div class = "ttdeci" > #define DISALLOW_COPY_AND_ASSIGN(TypeName)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "macros_8h_source.html#l00029" > macros.h:29< / a > < / div > < / div >
< div class = "ttc" id = "amarkowitz_8cc_html_aa9d6c98fdf8d89b0e2321fda02adc82c" > < div class = "ttname" > < a href = "markowitz_8cc.html#aa9d6c98fdf8d89b0e2321fda02adc82c" > col< / a > < / div > < div class = "ttdeci" > ColIndex col< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "markowitz_8cc_source.html#l00183" > markowitz.cc:183< / a > < / div > < / div >
< div class = "ttc" id = "amarkowitz_8cc_html_aea35f36ba98d5bbd8d033382f50c9e52" > < div class = "ttname" > < a href = "markowitz_8cc.html#aea35f36ba98d5bbd8d033382f50c9e52" > row< / a > < / div > < div class = "ttdeci" > RowIndex row< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "markowitz_8cc_source.html#l00182" > markowitz.cc:182< / a > < / div > < / div >
< div class = "ttc" id = "anamespaceoperations__research_1_1glop_html_a6b1b56ad0cb77edbd314f2bec33b467a" > < div class = "ttname" > < a href = "namespaceoperations__research_1_1glop.html#a6b1b56ad0cb77edbd314f2bec33b467a" > operations_research::glop::ColumnPermutation< / a > < / div > < div class = "ttdeci" > Permutation< ColIndex > ColumnPermutation< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "lp__data_2permutation_8h_source.html#l00095" > lp_data/permutation.h:95< / a > < / div > < / div >
< div class = "ttc" id = "anamespaceoperations__research_1_1glop_html_a733947145e3e1631165b618b05c9ccb7" > < div class = "ttname" > < a href = "namespaceoperations__research_1_1glop.html#a733947145e3e1631165b618b05c9ccb7" > operations_research::glop::Fractional< / a > < / div > < div class = "ttdeci" > double Fractional< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "lp__types_8h_source.html#l00078" > lp_types.h:78< / a > < / div > < / div >
< div class = "ttc" id = "anamespaceoperations__research_1_1glop_html_a8fbc9efd86a3cc862a9079d86ab8b524" > < div class = "ttname" > < a href = "namespaceoperations__research_1_1glop.html#a8fbc9efd86a3cc862a9079d86ab8b524" > operations_research::glop::RowToColIndex< / a > < / div > < div class = "ttdeci" > ColIndex RowToColIndex(RowIndex row)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "lp__types_8h_source.html#l00049" > lp_types.h:49< / a > < / div > < / div >
< div class = "ttc" id = "anamespaceoperations__research_1_1glop_html_ac014de658aabf122011e8fb07b6f4612" > < div class = "ttname" > < a href = "namespaceoperations__research_1_1glop.html#ac014de658aabf122011e8fb07b6f4612" > operations_research::glop::RowIndexVector< / a > < / div > < div class = "ttdeci" > std::vector< RowIndex > RowIndexVector< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "lp__types_8h_source.html#l00313" > lp_types.h:313< / a > < / div > < / div >
< div class = "ttc" id = "anamespaceoperations__research_1_1glop_html_ae69267cf0653a77925ee13121b9857ec" > < div class = "ttname" > < a href = "namespaceoperations__research_1_1glop.html#ae69267cf0653a77925ee13121b9857ec" > operations_research::glop::RowPermutation< / a > < / div > < div class = "ttdeci" > Permutation< RowIndex > RowPermutation< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "lp__data_2permutation_8h_source.html#l00094" > lp_data/permutation.h:94< / a > < / div > < / div >
2021-12-14 13:41:01 +01:00
< div class = "ttc" id = "anamespaceoperations__research_1_1sat_html_acb294633c7688f918623b3b0e09aec43" > < div class = "ttname" > < a href = "namespaceoperations__research_1_1sat.html#acb294633c7688f918623b3b0e09aec43" > operations_research::sat::ComputeInfinityNorm< / a > < / div > < div class = "ttdeci" > IntegerValue ComputeInfinityNorm(const LinearConstraint & constraint)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "sat_2linear__constraint_8cc_source.html#l00159" > sat/linear_constraint.cc:159< / a > < / div > < / div >
2021-09-30 01:18:45 +02:00
< div class = "ttc" id = "anamespaceoperations__research_html" > < div class = "ttname" > < a href = "namespaceoperations__research.html" > operations_research< / a > < / div > < div class = "ttdoc" > Collection of objects used to extend the Constraint Solver library.< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "dense__doubly__linked__list_8h_source.html#l00021" > dense_doubly_linked_list.h:21< / a > < / div > < / div >
< div class = "ttc" id = "aparser_8yy_8cc_html_a5a634cf4429798b1c921a81de8250051" > < div class = "ttname" > < a href = "parser_8yy_8cc.html#a5a634cf4429798b1c921a81de8250051" > input< / a > < / div > < div class = "ttdeci" > static int input(yyscan_t yyscanner)< / div > < / div >
2021-12-14 13:41:01 +01:00
< div class = "ttc" id = "apreprocessor_8cc_html_a2babe18010525bbf13c2fa5a959971e4" > < div class = "ttname" > < a href = "preprocessor_8cc.html#a2babe18010525bbf13c2fa5a959971e4" > num_entries< / a > < / div > < div class = "ttdeci" > EntryIndex num_entries< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "preprocessor_8cc_source.html#l01370" > preprocessor.cc:1370< / a > < / div > < / div >
2021-09-30 01:18:45 +02:00
< div class = "ttc" id = "areturn__macros_8h_html" > < div class = "ttname" > < a href = "return__macros_8h.html" > return_macros.h< / a > < / div > < / div >
< div class = "ttc" id = "areturn__macros_8h_html_a6009315499028d98072d8f31834cf4f9" > < div class = "ttname" > < a href = "return__macros_8h.html#a6009315499028d98072d8f31834cf4f9" > RETURN_IF_NULL< / a > < / div > < div class = "ttdeci" > #define RETURN_IF_NULL(x)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "return__macros_8h_source.html#l00020" > return_macros.h:20< / a > < / div > < / div >
< div class = "ttc" id = "ascattered__vector_8h_html" > < div class = "ttname" > < a href = "scattered__vector_8h.html" > scattered_vector.h< / a > < / div > < / div >
< div class = "ttc" id = "asparse__column_8h_html" > < div class = "ttname" > < a href = "sparse__column_8h.html" > sparse_column.h< / a > < / div > < / div >
< div class = "ttc" id = "astructoperations__research_1_1glop_1_1_scattered_column_html" > < div class = "ttname" > < a href = "structoperations__research_1_1glop_1_1_scattered_column.html" > operations_research::glop::ScatteredColumn< / a > < / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "scattered__vector_8h_source.html#l00196" > scattered_vector.h:197< / a > < / div > < / div >
< div class = "ttc" id = "astructoperations__research_1_1glop_1_1_scattered_vector_html_a9bb4f0967311f0f79a279879c4d69678" > < div class = "ttname" > < a href = "structoperations__research_1_1glop_1_1_scattered_vector.html#a9bb4f0967311f0f79a279879c4d69678" > operations_research::glop::ScatteredVector::Add< / a > < / div > < div class = "ttdeci" > void Add(Index index, Fractional value)< / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "scattered__vector_8h_source.html#l00098" > scattered_vector.h:98< / a > < / div > < / div >
2021-01-26 11:28:50 +01:00
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