747 lines
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747 lines
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<div class="headertitle"><div class="title">sharded_optimization_utils_test.cc</div></div>
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<a href="sharded__optimization__utils__test_8cc.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>
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<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>
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<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>
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<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>
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<div class="line"><a id="l00005" name="l00005"></a><span class="lineno"> 5</span><span class="comment">//</span></div>
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<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>
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<div class="line"><a id="l00007" name="l00007"></a><span class="lineno"> 7</span><span class="comment">//</span></div>
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<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>
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<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>
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<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>
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<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>
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<div class="line"><a id="l00012" name="l00012"></a><span class="lineno"> 12</span><span class="comment">// limitations under the License.</span></div>
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<div class="line"><a id="l00013" name="l00013"></a><span class="lineno"> 13</span> </div>
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<div class="line"><a id="l00014" name="l00014"></a><span class="lineno"> 14</span><span class="preprocessor">#include "<a class="code" href="sharded__optimization__utils_8h.html">ortools/pdlp/sharded_optimization_utils.h</a>"</span></div>
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<div class="line"><a id="l00015" name="l00015"></a><span class="lineno"> 15</span> </div>
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<div class="line"><a id="l00016" name="l00016"></a><span class="lineno"> 16</span><span class="preprocessor">#include <cmath></span></div>
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<div class="line"><a id="l00017" name="l00017"></a><span class="lineno"> 17</span><span class="preprocessor">#include <cstdint></span></div>
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<div class="line"><a id="l00018" name="l00018"></a><span class="lineno"> 18</span><span class="preprocessor">#include <random></span></div>
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<div class="line"><a id="l00019" name="l00019"></a><span class="lineno"> 19</span><span class="preprocessor">#include <utility></span></div>
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<div class="line"><a id="l00020" name="l00020"></a><span class="lineno"> 20</span><span class="preprocessor">#include <vector></span></div>
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<div class="line"><a id="l00021" name="l00021"></a><span class="lineno"> 21</span> </div>
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<div class="line"><a id="l00022" name="l00022"></a><span class="lineno"> 22</span><span class="preprocessor">#include "Eigen/Core"</span></div>
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<div class="line"><a id="l00023" name="l00023"></a><span class="lineno"> 23</span><span class="preprocessor">#include "Eigen/SparseCore"</span></div>
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<div class="line"><a id="l00024" name="l00024"></a><span class="lineno"> 24</span><span class="preprocessor">#include "absl/types/optional.h"</span></div>
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<div class="line"><a id="l00025" name="l00025"></a><span class="lineno"> 25</span><span class="preprocessor">#include "gmock/gmock.h"</span></div>
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<div class="line"><a id="l00026" name="l00026"></a><span class="lineno"> 26</span><span class="preprocessor">#include "gtest/gtest.h"</span></div>
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<div class="line"><a id="l00027" name="l00027"></a><span class="lineno"> 27</span><span class="preprocessor">#include "<a class="code" href="quadratic__program_8h.html">ortools/pdlp/quadratic_program.h</a>"</span></div>
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<div class="line"><a id="l00028" name="l00028"></a><span class="lineno"> 28</span><span class="preprocessor">#include "<a class="code" href="sharded__quadratic__program_8h.html">ortools/pdlp/sharded_quadratic_program.h</a>"</span></div>
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<div class="line"><a id="l00029" name="l00029"></a><span class="lineno"> 29</span><span class="preprocessor">#include "<a class="code" href="sharder_8h.html">ortools/pdlp/sharder.h</a>"</span></div>
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<div class="line"><a id="l00030" name="l00030"></a><span class="lineno"> 30</span><span class="preprocessor">#include "ortools/pdlp/solve_log.pb.h"</span></div>
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<div class="line"><a id="l00031" name="l00031"></a><span class="lineno"> 31</span><span class="preprocessor">#include "<a class="code" href="test__util_8h.html">ortools/pdlp/test_util.h</a>"</span></div>
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<div class="line"><a id="l00032" name="l00032"></a><span class="lineno"> 32</span> </div>
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<div class="line"><a id="l00033" name="l00033"></a><span class="lineno"> 33</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceoperations__research_1_1pdlp.html">operations_research::pdlp</a> {</div>
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<div class="line"><a id="l00034" name="l00034"></a><span class="lineno"> 34</span><span class="keyword">namespace </span>{</div>
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<div class="line"><a id="l00035" name="l00035"></a><span class="lineno"> 35</span> </div>
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<div class="line"><a id="l00036" name="l00036"></a><span class="lineno"> 36</span>using ::Eigen::VectorXd;</div>
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<div class="line"><a id="l00037" name="l00037"></a><span class="lineno"> 37</span>using ::testing::ElementsAre;</div>
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<div class="line"><a id="l00038" name="l00038"></a><span class="lineno"> 38</span>using ::testing::ElementsAreArray;</div>
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<div class="line"><a id="l00039" name="l00039"></a><span class="lineno"> 39</span>using ::testing::IsNan;</div>
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<div class="line"><a id="l00040" name="l00040"></a><span class="lineno"> 40</span> </div>
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<div class="line"><a id="l00041" name="l00041"></a><span class="lineno"> 41</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(ShardedWeightedAverageTest, SimpleAverage) {</div>
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<div class="line"><a id="l00042" name="l00042"></a><span class="lineno"> 42</span> Sharder sharder(<span class="comment">/*num_elements=*/</span>2, <span class="comment">/*num_shards=*/</span>2,</div>
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<div class="line"><a id="l00043" name="l00043"></a><span class="lineno"> 43</span> <span class="comment">/*thread_pool=*/</span><span class="keyword">nullptr</span>);</div>
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<div class="line"><a id="l00044" name="l00044"></a><span class="lineno"> 44</span> Eigen::VectorXd vec1(2), vec2(2);</div>
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<div class="line"><a id="l00045" name="l00045"></a><span class="lineno"> 45</span> vec1 << 4, 1;</div>
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<div class="line"><a id="l00046" name="l00046"></a><span class="lineno"> 46</span> vec2 << 1, 7;</div>
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<div class="line"><a id="l00047" name="l00047"></a><span class="lineno"> 47</span> </div>
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<div class="line"><a id="l00048" name="l00048"></a><span class="lineno"> 48</span> ShardedWeightedAverage <a class="code hl_variable" href="sharded__optimization__utils_8cc.html#a2e50537b138c27e2e30e8d3a568fbee0">average</a>(&sharder);</div>
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<div class="line"><a id="l00049" name="l00049"></a><span class="lineno"> 49</span> <a class="code hl_variable" href="sharded__optimization__utils_8cc.html#a2e50537b138c27e2e30e8d3a568fbee0">average</a>.Add(vec1, 1.0);</div>
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<div class="line"><a id="l00050" name="l00050"></a><span class="lineno"> 50</span> <a class="code hl_variable" href="sharded__optimization__utils_8cc.html#a2e50537b138c27e2e30e8d3a568fbee0">average</a>.Add(vec2, 2.0);</div>
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<div class="line"><a id="l00051" name="l00051"></a><span class="lineno"> 51</span> </div>
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<div class="line"><a id="l00052" name="l00052"></a><span class="lineno"> 52</span> ASSERT_TRUE(<a class="code hl_variable" href="sharded__optimization__utils_8cc.html#a2e50537b138c27e2e30e8d3a568fbee0">average</a>.HasNonzeroWeight());</div>
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<div class="line"><a id="l00053" name="l00053"></a><span class="lineno"> 53</span> EXPECT_EQ(<a class="code hl_variable" href="sharded__optimization__utils_8cc.html#a2e50537b138c27e2e30e8d3a568fbee0">average</a>.NumTerms(), 2);</div>
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<div class="line"><a id="l00054" name="l00054"></a><span class="lineno"> 54</span> </div>
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<div class="line"><a id="l00055" name="l00055"></a><span class="lineno"> 55</span> EXPECT_THAT(<a class="code hl_variable" href="sharded__optimization__utils_8cc.html#a2e50537b138c27e2e30e8d3a568fbee0">average</a>.ComputeAverage(), ElementsAre(2.0, 5.0));</div>
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<div class="line"><a id="l00056" name="l00056"></a><span class="lineno"> 56</span> </div>
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<div class="line"><a id="l00057" name="l00057"></a><span class="lineno"> 57</span> <a class="code hl_variable" href="sharded__optimization__utils_8cc.html#a2e50537b138c27e2e30e8d3a568fbee0">average</a>.Clear();</div>
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<div class="line"><a id="l00058" name="l00058"></a><span class="lineno"> 58</span> EXPECT_FALSE(<a class="code hl_variable" href="sharded__optimization__utils_8cc.html#a2e50537b138c27e2e30e8d3a568fbee0">average</a>.HasNonzeroWeight());</div>
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<div class="line"><a id="l00059" name="l00059"></a><span class="lineno"> 59</span> EXPECT_EQ(<a class="code hl_variable" href="sharded__optimization__utils_8cc.html#a2e50537b138c27e2e30e8d3a568fbee0">average</a>.NumTerms(), 0);</div>
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<div class="line"><a id="l00060" name="l00060"></a><span class="lineno"> 60</span>}</div>
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<div class="line"><a id="l00061" name="l00061"></a><span class="lineno"> 61</span> </div>
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<div class="line"><a id="l00062" name="l00062"></a><span class="lineno"> 62</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(ShardedWeightedAverageTest, MoveConstruction) {</div>
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<div class="line"><a id="l00063" name="l00063"></a><span class="lineno"> 63</span> Sharder sharder(<span class="comment">/*num_elements=*/</span>2, <span class="comment">/*num_shards=*/</span>2,</div>
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<div class="line"><a id="l00064" name="l00064"></a><span class="lineno"> 64</span> <span class="comment">/*thread_pool=*/</span><span class="keyword">nullptr</span>);</div>
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<div class="line"><a id="l00065" name="l00065"></a><span class="lineno"> 65</span> Eigen::VectorXd vec(2);</div>
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<div class="line"><a id="l00066" name="l00066"></a><span class="lineno"> 66</span> vec << 4, 1;</div>
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<div class="line"><a id="l00067" name="l00067"></a><span class="lineno"> 67</span> </div>
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<div class="line"><a id="l00068" name="l00068"></a><span class="lineno"> 68</span> ShardedWeightedAverage <a class="code hl_variable" href="sharded__optimization__utils_8cc.html#a2e50537b138c27e2e30e8d3a568fbee0">average</a>(&sharder);</div>
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<div class="line"><a id="l00069" name="l00069"></a><span class="lineno"> 69</span> <a class="code hl_variable" href="sharded__optimization__utils_8cc.html#a2e50537b138c27e2e30e8d3a568fbee0">average</a>.Add(vec, 2.0);</div>
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<div class="line"><a id="l00070" name="l00070"></a><span class="lineno"> 70</span> </div>
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<div class="line"><a id="l00071" name="l00071"></a><span class="lineno"> 71</span> ShardedWeightedAverage average2(std::move(<a class="code hl_variable" href="sharded__optimization__utils_8cc.html#a2e50537b138c27e2e30e8d3a568fbee0">average</a>));</div>
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<div class="line"><a id="l00072" name="l00072"></a><span class="lineno"> 72</span> EXPECT_THAT(average2.ComputeAverage(), ElementsAre(4.0, 1.0));</div>
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<div class="line"><a id="l00073" name="l00073"></a><span class="lineno"> 73</span>}</div>
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<div class="line"><a id="l00074" name="l00074"></a><span class="lineno"> 74</span> </div>
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<div class="line"><a id="l00075" name="l00075"></a><span class="lineno"> 75</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(ShardedWeightedAverageTest, MoveAssignment) {</div>
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<div class="line"><a id="l00076" name="l00076"></a><span class="lineno"> 76</span> Sharder sharder1(<span class="comment">/*num_elements=*/</span>2, <span class="comment">/*num_shards=*/</span>2,</div>
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<div class="line"><a id="l00077" name="l00077"></a><span class="lineno"> 77</span> <span class="comment">/*thread_pool=*/</span><span class="keyword">nullptr</span>);</div>
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<div class="line"><a id="l00078" name="l00078"></a><span class="lineno"> 78</span> Sharder sharder2(<span class="comment">/*num_elements=*/</span>3, <span class="comment">/*num_shards=*/</span>2,</div>
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<div class="line"><a id="l00079" name="l00079"></a><span class="lineno"> 79</span> <span class="comment">/*thread_pool=*/</span><span class="keyword">nullptr</span>);</div>
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<div class="line"><a id="l00080" name="l00080"></a><span class="lineno"> 80</span> Eigen::VectorXd vec1(2), vec2(2);</div>
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<div class="line"><a id="l00081" name="l00081"></a><span class="lineno"> 81</span> vec1 << 4, 1;</div>
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<div class="line"><a id="l00082" name="l00082"></a><span class="lineno"> 82</span> vec2 << 0, 3;</div>
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<div class="line"><a id="l00083" name="l00083"></a><span class="lineno"> 83</span> </div>
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<div class="line"><a id="l00084" name="l00084"></a><span class="lineno"> 84</span> ShardedWeightedAverage average1(&sharder1);</div>
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<div class="line"><a id="l00085" name="l00085"></a><span class="lineno"> 85</span> average1.Add(vec1, 2.0);</div>
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<div class="line"><a id="l00086" name="l00086"></a><span class="lineno"> 86</span> </div>
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<div class="line"><a id="l00087" name="l00087"></a><span class="lineno"> 87</span> ShardedWeightedAverage average2(&sharder2);</div>
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<div class="line"><a id="l00088" name="l00088"></a><span class="lineno"> 88</span> </div>
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<div class="line"><a id="l00089" name="l00089"></a><span class="lineno"> 89</span> average2 = std::move(average1);</div>
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<div class="line"><a id="l00090" name="l00090"></a><span class="lineno"> 90</span> average2.Add(vec2, 2.0);</div>
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<div class="line"><a id="l00091" name="l00091"></a><span class="lineno"> 91</span> EXPECT_THAT(average2.ComputeAverage(), ElementsAre(2.0, 2.0));</div>
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<div class="line"><a id="l00092" name="l00092"></a><span class="lineno"> 92</span>}</div>
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<div class="line"><a id="l00093" name="l00093"></a><span class="lineno"> 93</span> </div>
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<div class="line"><a id="l00094" name="l00094"></a><span class="lineno"> 94</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(ShardedWeightedAverageTest, ZeroAverage) {</div>
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<div class="line"><a id="l00095" name="l00095"></a><span class="lineno"> 95</span> Sharder sharder(<span class="comment">/*num_elements=*/</span>1, <span class="comment">/*num_shards=*/</span>1,</div>
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<div class="line"><a id="l00096" name="l00096"></a><span class="lineno"> 96</span> <span class="comment">/*thread_pool=*/</span><span class="keyword">nullptr</span>);</div>
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<div class="line"><a id="l00097" name="l00097"></a><span class="lineno"> 97</span> </div>
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<div class="line"><a id="l00098" name="l00098"></a><span class="lineno"> 98</span> ShardedWeightedAverage <a class="code hl_variable" href="sharded__optimization__utils_8cc.html#a2e50537b138c27e2e30e8d3a568fbee0">average</a>(&sharder);</div>
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<div class="line"><a id="l00099" name="l00099"></a><span class="lineno"> 99</span> ASSERT_FALSE(<a class="code hl_variable" href="sharded__optimization__utils_8cc.html#a2e50537b138c27e2e30e8d3a568fbee0">average</a>.HasNonzeroWeight());</div>
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<div class="line"><a id="l00100" name="l00100"></a><span class="lineno"> 100</span> </div>
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<div class="line"><a id="l00101" name="l00101"></a><span class="lineno"> 101</span> EXPECT_THAT(<a class="code hl_variable" href="sharded__optimization__utils_8cc.html#a2e50537b138c27e2e30e8d3a568fbee0">average</a>.ComputeAverage(), ElementsAre(0.0));</div>
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<div class="line"><a id="l00102" name="l00102"></a><span class="lineno"> 102</span>}</div>
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<div class="line"><a id="l00103" name="l00103"></a><span class="lineno"> 103</span> </div>
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<div class="line"><a id="l00104" name="l00104"></a><span class="lineno"> 104</span><span class="comment">// This test verifies that if we average an identical vector repeatedly the</span></div>
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<div class="line"><a id="l00105" name="l00105"></a><span class="lineno"> 105</span><span class="comment">// average is exactly that vector, with no roundoff.</span></div>
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<div class="line"><a id="l00106" name="l00106"></a><span class="lineno"> 106</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(ShardedWeightedAverageTest, AveragesEqualWithoutRoundoff) {</div>
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<div class="line"><a id="l00107" name="l00107"></a><span class="lineno"> 107</span> Sharder sharder(<span class="comment">/*num_elements=*/</span>4, <span class="comment">/*num_shards=*/</span>1,</div>
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<div class="line"><a id="l00108" name="l00108"></a><span class="lineno"> 108</span> <span class="comment">/*thread_pool=*/</span><span class="keyword">nullptr</span>);</div>
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<div class="line"><a id="l00109" name="l00109"></a><span class="lineno"> 109</span> ShardedWeightedAverage <a class="code hl_variable" href="sharded__optimization__utils_8cc.html#a2e50537b138c27e2e30e8d3a568fbee0">average</a>(&sharder);</div>
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<div class="line"><a id="l00110" name="l00110"></a><span class="lineno"> 110</span> EXPECT_THAT(<a class="code hl_variable" href="sharded__optimization__utils_8cc.html#a2e50537b138c27e2e30e8d3a568fbee0">average</a>.ComputeAverage(), ElementsAre(0, 0, 0, 0));</div>
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<div class="line"><a id="l00111" name="l00111"></a><span class="lineno"> 111</span> VectorXd data(4);</div>
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<div class="line"><a id="l00112" name="l00112"></a><span class="lineno"> 112</span> data << 1.0, 1.0 / 3, 3.0 / 7, 3.14159;</div>
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<div class="line"><a id="l00113" name="l00113"></a><span class="lineno"> 113</span> <a class="code hl_variable" href="sharded__optimization__utils_8cc.html#a2e50537b138c27e2e30e8d3a568fbee0">average</a>.Add(data, 341.45);</div>
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<div class="line"><a id="l00114" name="l00114"></a><span class="lineno"> 114</span> EXPECT_THAT(<a class="code hl_variable" href="sharded__optimization__utils_8cc.html#a2e50537b138c27e2e30e8d3a568fbee0">average</a>.ComputeAverage(), ElementsAreArray(data));</div>
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<div class="line"><a id="l00115" name="l00115"></a><span class="lineno"> 115</span> <a class="code hl_variable" href="sharded__optimization__utils_8cc.html#a2e50537b138c27e2e30e8d3a568fbee0">average</a>.Add(data, 1.4134);</div>
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<div class="line"><a id="l00116" name="l00116"></a><span class="lineno"> 116</span> EXPECT_THAT(<a class="code hl_variable" href="sharded__optimization__utils_8cc.html#a2e50537b138c27e2e30e8d3a568fbee0">average</a>.ComputeAverage(), ElementsAreArray(data));</div>
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<div class="line"><a id="l00117" name="l00117"></a><span class="lineno"> 117</span> <a class="code hl_variable" href="sharded__optimization__utils_8cc.html#a2e50537b138c27e2e30e8d3a568fbee0">average</a>.Add(data, 7.23);</div>
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<div class="line"><a id="l00118" name="l00118"></a><span class="lineno"> 118</span> EXPECT_THAT(<a class="code hl_variable" href="sharded__optimization__utils_8cc.html#a2e50537b138c27e2e30e8d3a568fbee0">average</a>.ComputeAverage(), ElementsAreArray(data));</div>
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<div class="line"><a id="l00119" name="l00119"></a><span class="lineno"> 119</span>}</div>
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<div class="line"><a id="l00120" name="l00120"></a><span class="lineno"> 120</span> </div>
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<div class="line"><a id="l00121" name="l00121"></a><span class="lineno"> 121</span><span class="comment">// The combined bounds vector for TestLp() is [12, 7, 4, 1].</span></div>
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<div class="line"><a id="l00122" name="l00122"></a><span class="lineno"> 122</span><span class="comment">// L_inf norm: 12.0</span></div>
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<div class="line"><a id="l00123" name="l00123"></a><span class="lineno"> 123</span><span class="comment">// L_2 norm: sqrt(210.0) ≈ 14.49</span></div>
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<div class="line"><a id="l00124" name="l00124"></a><span class="lineno"> 124</span> </div>
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<div class="line"><a id="l00125" name="l00125"></a><span class="lineno"> 125</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(ProblemStatsTest, <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ad795f23e2a85f4ae05efa1e2d0e0de4d">TestLp</a>) {</div>
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<div class="line"><a id="l00126" name="l00126"></a><span class="lineno"> 126</span> ShardedQuadraticProgram lp(<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ad795f23e2a85f4ae05efa1e2d0e0de4d">TestLp</a>(), 2, 2);</div>
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<div class="line"><a id="l00127" name="l00127"></a><span class="lineno"> 127</span> <span class="keyword">const</span> QuadraticProgramStats stats = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ab315578d37cb2f5e1111b0176254cb84">ComputeStats</a>(lp);</div>
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<div class="line"><a id="l00128" name="l00128"></a><span class="lineno"> 128</span> </div>
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<div class="line"><a id="l00129" name="l00129"></a><span class="lineno"> 129</span> EXPECT_EQ(stats.num_variables(), 4);</div>
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<div class="line"><a id="l00130" name="l00130"></a><span class="lineno"> 130</span> EXPECT_EQ(stats.num_constraints(), 4);</div>
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<div class="line"><a id="l00131" name="l00131"></a><span class="lineno"> 131</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_col_min_l_inf_norm(), 1.0);</div>
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<div class="line"><a id="l00132" name="l00132"></a><span class="lineno"> 132</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_row_min_l_inf_norm(), 1.0);</div>
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<div class="line"><a id="l00133" name="l00133"></a><span class="lineno"> 133</span> EXPECT_EQ(stats.constraint_matrix_num_nonzeros(), 9);</div>
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<div class="line"><a id="l00134" name="l00134"></a><span class="lineno"> 134</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_abs_max(), 4.0);</div>
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<div class="line"><a id="l00135" name="l00135"></a><span class="lineno"> 135</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_abs_min(), 1.0);</div>
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<div class="line"><a id="l00136" name="l00136"></a><span class="lineno"> 136</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_abs_avg(), 14.5 / 9.0);</div>
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<div class="line"><a id="l00137" name="l00137"></a><span class="lineno"> 137</span> EXPECT_DOUBLE_EQ(stats.objective_vector_abs_max(), 5.5);</div>
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<div class="line"><a id="l00138" name="l00138"></a><span class="lineno"> 138</span> EXPECT_DOUBLE_EQ(stats.objective_vector_abs_min(), 1.0);</div>
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<div class="line"><a id="l00139" name="l00139"></a><span class="lineno"> 139</span> EXPECT_DOUBLE_EQ(stats.objective_vector_abs_avg(), 2.375);</div>
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<div class="line"><a id="l00140" name="l00140"></a><span class="lineno"> 140</span> EXPECT_DOUBLE_EQ(stats.objective_vector_l2_norm(), std::sqrt(36.25));</div>
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<div class="line"><a id="l00141" name="l00141"></a><span class="lineno"> 141</span> EXPECT_EQ(stats.objective_matrix_num_nonzeros(), 0);</div>
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<div class="line"><a id="l00142" name="l00142"></a><span class="lineno"> 142</span> EXPECT_DOUBLE_EQ(stats.objective_matrix_abs_max(), 0.0);</div>
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<div class="line"><a id="l00143" name="l00143"></a><span class="lineno"> 143</span> EXPECT_DOUBLE_EQ(stats.objective_matrix_abs_min(), 0.0);</div>
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<div class="line"><a id="l00144" name="l00144"></a><span class="lineno"> 144</span> EXPECT_THAT(stats.objective_matrix_abs_avg(), IsNan());</div>
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<div class="line"><a id="l00145" name="l00145"></a><span class="lineno"> 145</span> EXPECT_EQ(stats.variable_bound_gaps_num_finite(), 1);</div>
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<div class="line"><a id="l00146" name="l00146"></a><span class="lineno"> 146</span> EXPECT_DOUBLE_EQ(stats.variable_bound_gaps_max(), 1.0);</div>
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<div class="line"><a id="l00147" name="l00147"></a><span class="lineno"> 147</span> EXPECT_DOUBLE_EQ(stats.variable_bound_gaps_min(), 1.0);</div>
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<div class="line"><a id="l00148" name="l00148"></a><span class="lineno"> 148</span> EXPECT_DOUBLE_EQ(stats.variable_bound_gaps_avg(), 1.0);</div>
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<div class="line"><a id="l00149" name="l00149"></a><span class="lineno"> 149</span> EXPECT_DOUBLE_EQ(stats.combined_bounds_max(), 12.0);</div>
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<div class="line"><a id="l00150" name="l00150"></a><span class="lineno"> 150</span> EXPECT_DOUBLE_EQ(stats.combined_bounds_min(), 1.0);</div>
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<div class="line"><a id="l00151" name="l00151"></a><span class="lineno"> 151</span> EXPECT_DOUBLE_EQ(stats.combined_bounds_avg(), 6.0);</div>
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<div class="line"><a id="l00152" name="l00152"></a><span class="lineno"> 152</span> EXPECT_DOUBLE_EQ(stats.combined_bounds_l2_norm(), std::sqrt(210.0));</div>
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<div class="line"><a id="l00153" name="l00153"></a><span class="lineno"> 153</span>}</div>
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<div class="line"><a id="l00154" name="l00154"></a><span class="lineno"> 154</span> </div>
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<div class="line"><a id="l00155" name="l00155"></a><span class="lineno"> 155</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(ProblemStatsTest, <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a79496743a139659305201925cdcb39fa">TinyLp</a>) {</div>
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<div class="line"><a id="l00156" name="l00156"></a><span class="lineno"> 156</span> ShardedQuadraticProgram lp(<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a79496743a139659305201925cdcb39fa">TinyLp</a>(), 2, 2);</div>
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<div class="line"><a id="l00157" name="l00157"></a><span class="lineno"> 157</span> <span class="keyword">const</span> QuadraticProgramStats stats = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ab315578d37cb2f5e1111b0176254cb84">ComputeStats</a>(lp);</div>
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<div class="line"><a id="l00158" name="l00158"></a><span class="lineno"> 158</span> </div>
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<div class="line"><a id="l00159" name="l00159"></a><span class="lineno"> 159</span> EXPECT_EQ(stats.num_variables(), 4);</div>
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<div class="line"><a id="l00160" name="l00160"></a><span class="lineno"> 160</span> EXPECT_EQ(stats.num_constraints(), 3);</div>
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<div class="line"><a id="l00161" name="l00161"></a><span class="lineno"> 161</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_col_min_l_inf_norm(), 1.0);</div>
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<div class="line"><a id="l00162" name="l00162"></a><span class="lineno"> 162</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_row_min_l_inf_norm(), 1.0);</div>
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<div class="line"><a id="l00163" name="l00163"></a><span class="lineno"> 163</span> EXPECT_EQ(stats.constraint_matrix_num_nonzeros(), 8);</div>
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<div class="line"><a id="l00164" name="l00164"></a><span class="lineno"> 164</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_abs_max(), 2.0);</div>
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<div class="line"><a id="l00165" name="l00165"></a><span class="lineno"> 165</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_abs_min(), 1.0);</div>
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<div class="line"><a id="l00166" name="l00166"></a><span class="lineno"> 166</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_abs_avg(), 1.25);</div>
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<div class="line"><a id="l00167" name="l00167"></a><span class="lineno"> 167</span> EXPECT_DOUBLE_EQ(stats.objective_vector_abs_max(), 5.0);</div>
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<div class="line"><a id="l00168" name="l00168"></a><span class="lineno"> 168</span> EXPECT_DOUBLE_EQ(stats.objective_vector_abs_min(), 1.0);</div>
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<div class="line"><a id="l00169" name="l00169"></a><span class="lineno"> 169</span> EXPECT_DOUBLE_EQ(stats.objective_vector_abs_avg(), 2.25);</div>
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<div class="line"><a id="l00170" name="l00170"></a><span class="lineno"> 170</span> EXPECT_DOUBLE_EQ(stats.objective_vector_l2_norm(), std::sqrt(31.0));</div>
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<div class="line"><a id="l00171" name="l00171"></a><span class="lineno"> 171</span> EXPECT_EQ(stats.objective_matrix_num_nonzeros(), 0);</div>
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<div class="line"><a id="l00172" name="l00172"></a><span class="lineno"> 172</span> EXPECT_DOUBLE_EQ(stats.objective_matrix_abs_max(), 0.0);</div>
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<div class="line"><a id="l00173" name="l00173"></a><span class="lineno"> 173</span> EXPECT_DOUBLE_EQ(stats.objective_matrix_abs_min(), 0.0);</div>
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<div class="line"><a id="l00174" name="l00174"></a><span class="lineno"> 174</span> EXPECT_THAT(stats.objective_matrix_abs_avg(), IsNan());</div>
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<div class="line"><a id="l00175" name="l00175"></a><span class="lineno"> 175</span> EXPECT_EQ(stats.variable_bound_gaps_num_finite(), 4);</div>
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<div class="line"><a id="l00176" name="l00176"></a><span class="lineno"> 176</span> EXPECT_DOUBLE_EQ(stats.variable_bound_gaps_max(), 6.0);</div>
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<div class="line"><a id="l00177" name="l00177"></a><span class="lineno"> 177</span> EXPECT_DOUBLE_EQ(stats.variable_bound_gaps_min(), 2.0);</div>
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<div class="line"><a id="l00178" name="l00178"></a><span class="lineno"> 178</span> EXPECT_DOUBLE_EQ(stats.variable_bound_gaps_avg(), 3.75);</div>
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<div class="line"><a id="l00179" name="l00179"></a><span class="lineno"> 179</span> EXPECT_DOUBLE_EQ(stats.combined_bounds_max(), 12.0);</div>
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<div class="line"><a id="l00180" name="l00180"></a><span class="lineno"> 180</span> EXPECT_DOUBLE_EQ(stats.combined_bounds_min(), 1.0);</div>
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<div class="line"><a id="l00181" name="l00181"></a><span class="lineno"> 181</span> EXPECT_DOUBLE_EQ(stats.combined_bounds_avg(), 20.0 / 3.0);</div>
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<div class="line"><a id="l00182" name="l00182"></a><span class="lineno"> 182</span> EXPECT_DOUBLE_EQ(stats.combined_bounds_l2_norm(), std::sqrt(194.0));</div>
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<div class="line"><a id="l00183" name="l00183"></a><span class="lineno"> 183</span>}</div>
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<div class="line"><a id="l00184" name="l00184"></a><span class="lineno"> 184</span> </div>
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<div class="line"><a id="l00185" name="l00185"></a><span class="lineno"> 185</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(ProblemStatsTest, <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#afaedaf1e3ebe4d6d1a36e3fd1f206de6">TestDiagonalQp1</a>) {</div>
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<div class="line"><a id="l00186" name="l00186"></a><span class="lineno"> 186</span> ShardedQuadraticProgram qp(<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#afaedaf1e3ebe4d6d1a36e3fd1f206de6">TestDiagonalQp1</a>(), 2, 2);</div>
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<div class="line"><a id="l00187" name="l00187"></a><span class="lineno"> 187</span> <span class="keyword">const</span> QuadraticProgramStats stats = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ab315578d37cb2f5e1111b0176254cb84">ComputeStats</a>(qp);</div>
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<div class="line"><a id="l00188" name="l00188"></a><span class="lineno"> 188</span> </div>
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<div class="line"><a id="l00189" name="l00189"></a><span class="lineno"> 189</span> EXPECT_EQ(stats.num_variables(), 2);</div>
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<div class="line"><a id="l00190" name="l00190"></a><span class="lineno"> 190</span> EXPECT_EQ(stats.num_constraints(), 1);</div>
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<div class="line"><a id="l00191" name="l00191"></a><span class="lineno"> 191</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_col_min_l_inf_norm(), 1.0);</div>
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<div class="line"><a id="l00192" name="l00192"></a><span class="lineno"> 192</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_row_min_l_inf_norm(), 1.0);</div>
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<div class="line"><a id="l00193" name="l00193"></a><span class="lineno"> 193</span> EXPECT_EQ(stats.constraint_matrix_num_nonzeros(), 2);</div>
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<div class="line"><a id="l00194" name="l00194"></a><span class="lineno"> 194</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_abs_max(), 1.0);</div>
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<div class="line"><a id="l00195" name="l00195"></a><span class="lineno"> 195</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_abs_min(), 1.0);</div>
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<div class="line"><a id="l00196" name="l00196"></a><span class="lineno"> 196</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_abs_avg(), 1.0);</div>
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<div class="line"><a id="l00197" name="l00197"></a><span class="lineno"> 197</span> EXPECT_DOUBLE_EQ(stats.objective_vector_abs_max(), 1.0);</div>
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<div class="line"><a id="l00198" name="l00198"></a><span class="lineno"> 198</span> EXPECT_DOUBLE_EQ(stats.objective_vector_abs_min(), 1.0);</div>
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<div class="line"><a id="l00199" name="l00199"></a><span class="lineno"> 199</span> EXPECT_DOUBLE_EQ(stats.objective_vector_abs_avg(), 1.0);</div>
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<div class="line"><a id="l00200" name="l00200"></a><span class="lineno"> 200</span> EXPECT_DOUBLE_EQ(stats.objective_vector_l2_norm(), std::sqrt(2.0));</div>
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<div class="line"><a id="l00201" name="l00201"></a><span class="lineno"> 201</span> EXPECT_EQ(stats.objective_matrix_num_nonzeros(), 2);</div>
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<div class="line"><a id="l00202" name="l00202"></a><span class="lineno"> 202</span> EXPECT_DOUBLE_EQ(stats.objective_matrix_abs_max(), 4.0);</div>
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<div class="line"><a id="l00203" name="l00203"></a><span class="lineno"> 203</span> EXPECT_DOUBLE_EQ(stats.objective_matrix_abs_min(), 1.0);</div>
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<div class="line"><a id="l00204" name="l00204"></a><span class="lineno"> 204</span> EXPECT_DOUBLE_EQ(stats.objective_matrix_abs_avg(), 2.5);</div>
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<div class="line"><a id="l00205" name="l00205"></a><span class="lineno"> 205</span> EXPECT_EQ(stats.variable_bound_gaps_num_finite(), 2);</div>
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<div class="line"><a id="l00206" name="l00206"></a><span class="lineno"> 206</span> EXPECT_DOUBLE_EQ(stats.variable_bound_gaps_max(), 6.0);</div>
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<div class="line"><a id="l00207" name="l00207"></a><span class="lineno"> 207</span> EXPECT_DOUBLE_EQ(stats.variable_bound_gaps_min(), 1.0);</div>
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<div class="line"><a id="l00208" name="l00208"></a><span class="lineno"> 208</span> EXPECT_DOUBLE_EQ(stats.variable_bound_gaps_avg(), 3.5);</div>
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<div class="line"><a id="l00209" name="l00209"></a><span class="lineno"> 209</span> EXPECT_DOUBLE_EQ(stats.combined_bounds_max(), 1.0);</div>
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<div class="line"><a id="l00210" name="l00210"></a><span class="lineno"> 210</span> EXPECT_DOUBLE_EQ(stats.combined_bounds_min(), 1.0);</div>
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<div class="line"><a id="l00211" name="l00211"></a><span class="lineno"> 211</span> EXPECT_DOUBLE_EQ(stats.combined_bounds_avg(), 1.0);</div>
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<div class="line"><a id="l00212" name="l00212"></a><span class="lineno"> 212</span> EXPECT_DOUBLE_EQ(stats.combined_bounds_l2_norm(), 1.0);</div>
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<div class="line"><a id="l00213" name="l00213"></a><span class="lineno"> 213</span>}</div>
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<div class="line"><a id="l00214" name="l00214"></a><span class="lineno"> 214</span> </div>
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<div class="line"><a id="l00215" name="l00215"></a><span class="lineno"> 215</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(ProblemStatsTest, ModifiedTestDiagonalQp1) {</div>
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<div class="line"><a id="l00216" name="l00216"></a><span class="lineno"> 216</span> QuadraticProgram orig_qp = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#afaedaf1e3ebe4d6d1a36e3fd1f206de6">TestDiagonalQp1</a>();</div>
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<div class="line"><a id="l00217" name="l00217"></a><span class="lineno"> 217</span> <span class="comment">// A case where objective_matrix_num_nonzeros doesn't match the dimension.</span></div>
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<div class="line"><a id="l00218" name="l00218"></a><span class="lineno"> 218</span> orig_qp.objective_matrix->diagonal() << 2.0, 0.0;</div>
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<div class="line"><a id="l00219" name="l00219"></a><span class="lineno"> 219</span> ShardedQuadraticProgram qp(orig_qp, 2, 2);</div>
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<div class="line"><a id="l00220" name="l00220"></a><span class="lineno"> 220</span> <span class="keyword">const</span> QuadraticProgramStats stats = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ab315578d37cb2f5e1111b0176254cb84">ComputeStats</a>(qp);</div>
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<div class="line"><a id="l00221" name="l00221"></a><span class="lineno"> 221</span> </div>
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<div class="line"><a id="l00222" name="l00222"></a><span class="lineno"> 222</span> EXPECT_EQ(stats.objective_matrix_num_nonzeros(), 1);</div>
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<div class="line"><a id="l00223" name="l00223"></a><span class="lineno"> 223</span> EXPECT_DOUBLE_EQ(stats.objective_matrix_abs_max(), 2.0);</div>
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<div class="line"><a id="l00224" name="l00224"></a><span class="lineno"> 224</span> EXPECT_DOUBLE_EQ(stats.objective_matrix_abs_min(), 0.0);</div>
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<div class="line"><a id="l00225" name="l00225"></a><span class="lineno"> 225</span> EXPECT_DOUBLE_EQ(stats.objective_matrix_abs_avg(), 1.0);</div>
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<div class="line"><a id="l00226" name="l00226"></a><span class="lineno"> 226</span>}</div>
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<div class="line"><a id="l00227" name="l00227"></a><span class="lineno"> 227</span> </div>
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<div class="line"><a id="l00228" name="l00228"></a><span class="lineno"> 228</span><span class="comment">// This is like SmallLp, except that an infinite_bound_threshold of 10 treats</span></div>
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<div class="line"><a id="l00229" name="l00229"></a><span class="lineno"> 229</span><span class="comment">// the first bound as infinite, leaving [7, 4, 1] as the combined bounds vector.</span></div>
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<div class="line"><a id="l00230" name="l00230"></a><span class="lineno"> 230</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(ProblemStatsTest, TestLpWithInfiniteConstraintBoundThreshold) {</div>
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<div class="line"><a id="l00231" name="l00231"></a><span class="lineno"> 231</span> ShardedQuadraticProgram lp(<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ad795f23e2a85f4ae05efa1e2d0e0de4d">TestLp</a>(), 2, 2);</div>
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<div class="line"><a id="l00232" name="l00232"></a><span class="lineno"> 232</span> <span class="keyword">const</span> QuadraticProgramStats stats =</div>
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<div class="line"><a id="l00233" name="l00233"></a><span class="lineno"> 233</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ab315578d37cb2f5e1111b0176254cb84">ComputeStats</a>(lp, <span class="comment">/*infinite_constraint_bound_threshold=*/</span>10);</div>
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<div class="line"><a id="l00234" name="l00234"></a><span class="lineno"> 234</span> </div>
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<div class="line"><a id="l00235" name="l00235"></a><span class="lineno"> 235</span> EXPECT_EQ(stats.num_variables(), 4);</div>
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<div class="line"><a id="l00236" name="l00236"></a><span class="lineno"> 236</span> EXPECT_EQ(stats.num_constraints(), 4);</div>
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<div class="line"><a id="l00237" name="l00237"></a><span class="lineno"> 237</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_col_min_l_inf_norm(), 1.0);</div>
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<div class="line"><a id="l00238" name="l00238"></a><span class="lineno"> 238</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_row_min_l_inf_norm(), 1.0);</div>
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<div class="line"><a id="l00239" name="l00239"></a><span class="lineno"> 239</span> EXPECT_EQ(stats.constraint_matrix_num_nonzeros(), 9);</div>
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<div class="line"><a id="l00240" name="l00240"></a><span class="lineno"> 240</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_abs_max(), 4.0);</div>
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<div class="line"><a id="l00241" name="l00241"></a><span class="lineno"> 241</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_abs_min(), 1.0);</div>
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<div class="line"><a id="l00242" name="l00242"></a><span class="lineno"> 242</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_abs_avg(), 14.5 / 9.0);</div>
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<div class="line"><a id="l00243" name="l00243"></a><span class="lineno"> 243</span> EXPECT_DOUBLE_EQ(stats.objective_vector_abs_max(), 5.5);</div>
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<div class="line"><a id="l00244" name="l00244"></a><span class="lineno"> 244</span> EXPECT_DOUBLE_EQ(stats.objective_vector_abs_min(), 1.0);</div>
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<div class="line"><a id="l00245" name="l00245"></a><span class="lineno"> 245</span> EXPECT_DOUBLE_EQ(stats.objective_vector_abs_avg(), 2.375);</div>
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<div class="line"><a id="l00246" name="l00246"></a><span class="lineno"> 246</span> EXPECT_DOUBLE_EQ(stats.objective_vector_l2_norm(), std::sqrt(36.25));</div>
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<div class="line"><a id="l00247" name="l00247"></a><span class="lineno"> 247</span> EXPECT_EQ(stats.objective_matrix_num_nonzeros(), 0);</div>
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<div class="line"><a id="l00248" name="l00248"></a><span class="lineno"> 248</span> EXPECT_DOUBLE_EQ(stats.objective_matrix_abs_max(), 0.0);</div>
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<div class="line"><a id="l00249" name="l00249"></a><span class="lineno"> 249</span> EXPECT_DOUBLE_EQ(stats.objective_matrix_abs_min(), 0.0);</div>
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<div class="line"><a id="l00250" name="l00250"></a><span class="lineno"> 250</span> EXPECT_THAT(stats.objective_matrix_abs_avg(), IsNan());</div>
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<div class="line"><a id="l00251" name="l00251"></a><span class="lineno"> 251</span> EXPECT_EQ(stats.variable_bound_gaps_num_finite(), 1);</div>
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<div class="line"><a id="l00252" name="l00252"></a><span class="lineno"> 252</span> EXPECT_DOUBLE_EQ(stats.variable_bound_gaps_max(), 1.0);</div>
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<div class="line"><a id="l00253" name="l00253"></a><span class="lineno"> 253</span> EXPECT_DOUBLE_EQ(stats.variable_bound_gaps_min(), 1.0);</div>
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<div class="line"><a id="l00254" name="l00254"></a><span class="lineno"> 254</span> EXPECT_DOUBLE_EQ(stats.variable_bound_gaps_avg(), 1.0);</div>
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<div class="line"><a id="l00255" name="l00255"></a><span class="lineno"> 255</span> EXPECT_DOUBLE_EQ(stats.combined_bounds_max(), 7.0);</div>
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<div class="line"><a id="l00256" name="l00256"></a><span class="lineno"> 256</span> EXPECT_DOUBLE_EQ(stats.combined_bounds_min(), 0.0);</div>
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<div class="line"><a id="l00257" name="l00257"></a><span class="lineno"> 257</span> EXPECT_DOUBLE_EQ(stats.combined_bounds_avg(), 3.0);</div>
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<div class="line"><a id="l00258" name="l00258"></a><span class="lineno"> 258</span> EXPECT_DOUBLE_EQ(stats.combined_bounds_l2_norm(), std::sqrt(66.0));</div>
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<div class="line"><a id="l00259" name="l00259"></a><span class="lineno"> 259</span>}</div>
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<div class="line"><a id="l00260" name="l00260"></a><span class="lineno"> 260</span> </div>
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<div class="line"><a id="l00261" name="l00261"></a><span class="lineno"> 261</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(ProblemStatsTest, NoFiniteGaps) {</div>
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<div class="line"><a id="l00262" name="l00262"></a><span class="lineno"> 262</span> ShardedQuadraticProgram lp(<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a90ad9eef4ca03500a77b99e9e29a675f">SmallInvalidProblemLp</a>(), 2, 2);</div>
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<div class="line"><a id="l00263" name="l00263"></a><span class="lineno"> 263</span> <span class="keyword">const</span> QuadraticProgramStats stats = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ab315578d37cb2f5e1111b0176254cb84">ComputeStats</a>(lp);</div>
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<div class="line"><a id="l00264" name="l00264"></a><span class="lineno"> 264</span> <span class="comment">// Ensure max/min/avg take their default values when no finite gaps exist.</span></div>
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<div class="line"><a id="l00265" name="l00265"></a><span class="lineno"> 265</span> EXPECT_EQ(stats.variable_bound_gaps_num_finite(), 0);</div>
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<div class="line"><a id="l00266" name="l00266"></a><span class="lineno"> 266</span> EXPECT_DOUBLE_EQ(stats.variable_bound_gaps_max(), 0.0);</div>
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<div class="line"><a id="l00267" name="l00267"></a><span class="lineno"> 267</span> EXPECT_DOUBLE_EQ(stats.variable_bound_gaps_min(), 0.0);</div>
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<div class="line"><a id="l00268" name="l00268"></a><span class="lineno"> 268</span> EXPECT_THAT(stats.variable_bound_gaps_avg(), IsNan());</div>
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<div class="line"><a id="l00269" name="l00269"></a><span class="lineno"> 269</span>}</div>
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<div class="line"><a id="l00270" name="l00270"></a><span class="lineno"> 270</span> </div>
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<div class="line"><a id="l00271" name="l00271"></a><span class="lineno"> 271</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(ProblemStatsTest, <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a8d1b6834fa651b584ac83326d6a283b9">LpWithoutConstraints</a>) {</div>
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<div class="line"><a id="l00272" name="l00272"></a><span class="lineno"> 272</span> ShardedQuadraticProgram lp(<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a8d1b6834fa651b584ac83326d6a283b9">LpWithoutConstraints</a>(), 2, 2);</div>
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<div class="line"><a id="l00273" name="l00273"></a><span class="lineno"> 273</span> <span class="keyword">const</span> QuadraticProgramStats stats = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ab315578d37cb2f5e1111b0176254cb84">ComputeStats</a>(lp);</div>
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<div class="line"><a id="l00274" name="l00274"></a><span class="lineno"> 274</span> <span class="comment">// When there are no constraints, max/min absolute values and infinity norms</span></div>
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<div class="line"><a id="l00275" name="l00275"></a><span class="lineno"> 275</span> <span class="comment">// are assigned 0 by convention. The same is true for the combined bounds.</span></div>
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<div class="line"><a id="l00276" name="l00276"></a><span class="lineno"> 276</span> EXPECT_EQ(stats.constraint_matrix_num_nonzeros(), 0);</div>
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<div class="line"><a id="l00277" name="l00277"></a><span class="lineno"> 277</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_abs_max(), 0.0);</div>
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<div class="line"><a id="l00278" name="l00278"></a><span class="lineno"> 278</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_abs_min(), 0.0);</div>
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<div class="line"><a id="l00279" name="l00279"></a><span class="lineno"> 279</span> EXPECT_THAT(stats.constraint_matrix_abs_avg(), IsNan());</div>
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<div class="line"><a id="l00280" name="l00280"></a><span class="lineno"> 280</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_col_min_l_inf_norm(), 0.0);</div>
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<div class="line"><a id="l00281" name="l00281"></a><span class="lineno"> 281</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_row_min_l_inf_norm(), 0.0);</div>
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<div class="line"><a id="l00282" name="l00282"></a><span class="lineno"> 282</span> EXPECT_DOUBLE_EQ(stats.combined_bounds_max(), 0.0);</div>
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<div class="line"><a id="l00283" name="l00283"></a><span class="lineno"> 283</span> EXPECT_DOUBLE_EQ(stats.combined_bounds_min(), 0.0);</div>
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<div class="line"><a id="l00284" name="l00284"></a><span class="lineno"> 284</span> EXPECT_THAT(stats.combined_bounds_avg(), IsNan());</div>
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<div class="line"><a id="l00285" name="l00285"></a><span class="lineno"> 285</span>}</div>
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<div class="line"><a id="l00286" name="l00286"></a><span class="lineno"> 286</span> </div>
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<div class="line"><a id="l00287" name="l00287"></a><span class="lineno"> 287</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(ProblemStatsTest, EmptyLp) {</div>
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<div class="line"><a id="l00288" name="l00288"></a><span class="lineno"> 288</span> ShardedQuadraticProgram lp(QuadraticProgram(0, 0), 2, 2);</div>
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<div class="line"><a id="l00289" name="l00289"></a><span class="lineno"> 289</span> <span class="keyword">const</span> QuadraticProgramStats stats = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ab315578d37cb2f5e1111b0176254cb84">ComputeStats</a>(lp);</div>
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<div class="line"><a id="l00290" name="l00290"></a><span class="lineno"> 290</span> <span class="comment">// When LP is empty, everything except averages should be 0 and averages</span></div>
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<div class="line"><a id="l00291" name="l00291"></a><span class="lineno"> 291</span> <span class="comment">// should be NaN</span></div>
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<div class="line"><a id="l00292" name="l00292"></a><span class="lineno"> 292</span> EXPECT_EQ(stats.num_variables(), 0);</div>
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<div class="line"><a id="l00293" name="l00293"></a><span class="lineno"> 293</span> EXPECT_EQ(stats.num_constraints(), 0);</div>
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<div class="line"><a id="l00294" name="l00294"></a><span class="lineno"> 294</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_col_min_l_inf_norm(), 0.0);</div>
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<div class="line"><a id="l00295" name="l00295"></a><span class="lineno"> 295</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_row_min_l_inf_norm(), 0.0);</div>
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<div class="line"><a id="l00296" name="l00296"></a><span class="lineno"> 296</span> EXPECT_EQ(stats.constraint_matrix_num_nonzeros(), 0);</div>
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<div class="line"><a id="l00297" name="l00297"></a><span class="lineno"> 297</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_abs_max(), 0.0);</div>
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<div class="line"><a id="l00298" name="l00298"></a><span class="lineno"> 298</span> EXPECT_DOUBLE_EQ(stats.constraint_matrix_abs_min(), 0.0);</div>
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<div class="line"><a id="l00299" name="l00299"></a><span class="lineno"> 299</span> EXPECT_THAT(stats.constraint_matrix_abs_avg(), IsNan());</div>
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<div class="line"><a id="l00300" name="l00300"></a><span class="lineno"> 300</span> EXPECT_DOUBLE_EQ(stats.objective_vector_abs_max(), 0.0);</div>
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<div class="line"><a id="l00301" name="l00301"></a><span class="lineno"> 301</span> EXPECT_DOUBLE_EQ(stats.objective_vector_abs_min(), 0.0);</div>
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<div class="line"><a id="l00302" name="l00302"></a><span class="lineno"> 302</span> EXPECT_THAT(stats.objective_vector_abs_avg(), IsNan());</div>
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<div class="line"><a id="l00303" name="l00303"></a><span class="lineno"> 303</span> EXPECT_DOUBLE_EQ(stats.objective_vector_l2_norm(), 0.0);</div>
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<div class="line"><a id="l00304" name="l00304"></a><span class="lineno"> 304</span> EXPECT_EQ(stats.objective_matrix_num_nonzeros(), 0);</div>
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<div class="line"><a id="l00305" name="l00305"></a><span class="lineno"> 305</span> EXPECT_DOUBLE_EQ(stats.objective_matrix_abs_max(), 0.0);</div>
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<div class="line"><a id="l00306" name="l00306"></a><span class="lineno"> 306</span> EXPECT_DOUBLE_EQ(stats.objective_matrix_abs_min(), 0.0);</div>
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<div class="line"><a id="l00307" name="l00307"></a><span class="lineno"> 307</span> EXPECT_THAT(stats.objective_matrix_abs_avg(), IsNan());</div>
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<div class="line"><a id="l00308" name="l00308"></a><span class="lineno"> 308</span> EXPECT_EQ(stats.variable_bound_gaps_num_finite(), 0);</div>
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<div class="line"><a id="l00309" name="l00309"></a><span class="lineno"> 309</span> EXPECT_DOUBLE_EQ(stats.variable_bound_gaps_max(), 0.0);</div>
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<div class="line"><a id="l00310" name="l00310"></a><span class="lineno"> 310</span> EXPECT_DOUBLE_EQ(stats.variable_bound_gaps_min(), 0.0);</div>
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<div class="line"><a id="l00311" name="l00311"></a><span class="lineno"> 311</span> EXPECT_THAT(stats.variable_bound_gaps_avg(), IsNan());</div>
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<div class="line"><a id="l00312" name="l00312"></a><span class="lineno"> 312</span> EXPECT_DOUBLE_EQ(stats.combined_bounds_max(), 0.0);</div>
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<div class="line"><a id="l00313" name="l00313"></a><span class="lineno"> 313</span> EXPECT_DOUBLE_EQ(stats.combined_bounds_min(), 0.0);</div>
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<div class="line"><a id="l00314" name="l00314"></a><span class="lineno"> 314</span> EXPECT_THAT(stats.combined_bounds_avg(), IsNan());</div>
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<div class="line"><a id="l00315" name="l00315"></a><span class="lineno"> 315</span> EXPECT_DOUBLE_EQ(stats.combined_bounds_l2_norm(), 0.0);</div>
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<div class="line"><a id="l00316" name="l00316"></a><span class="lineno"> 316</span>}</div>
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<div class="line"><a id="l00317" name="l00317"></a><span class="lineno"> 317</span> </div>
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<div class="line"><a id="l00318" name="l00318"></a><span class="lineno"> 318</span><span class="comment">// The test_lp matrix is [ 2 1 1 2; 1 0 1 0; 4 0 0 0; 0 0 1.5 -1],</span></div>
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<div class="line"><a id="l00319" name="l00319"></a><span class="lineno"> 319</span><span class="comment">// the scaled matrix is [ 0 1 2 -2; 0 0 4 0; 0 0 0 0; 0 0 9 3],</span></div>
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<div class="line"><a id="l00320" name="l00320"></a><span class="lineno"> 320</span><span class="comment">// so the row LInf norms are [2 4 0 9] and the column LInf norms are [0 1 9 3].</span></div>
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<div class="line"><a id="l00321" name="l00321"></a><span class="lineno"> 321</span><span class="comment">// Rescaling divides the scaling vectors by sqrt(norms).</span></div>
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<div class="line"><a id="l00322" name="l00322"></a><span class="lineno"> 322</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a54ded6625965f8ddd342161a55263cce">LInfRuizRescaling</a>, OneIteration) {</div>
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<div class="line"><a id="l00323" name="l00323"></a><span class="lineno"> 323</span> ShardedQuadraticProgram lp(<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ad795f23e2a85f4ae05efa1e2d0e0de4d">TestLp</a>(), <span class="comment">/*num_threads=*/</span>2, <span class="comment">/*num_shards=*/</span>2);</div>
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<div class="line"><a id="l00324" name="l00324"></a><span class="lineno"> 324</span> VectorXd row_scaling_vec(4), col_scaling_vec(4);</div>
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<div class="line"><a id="l00325" name="l00325"></a><span class="lineno"> 325</span> row_scaling_vec << 1, 2, 1, 3;</div>
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<div class="line"><a id="l00326" name="l00326"></a><span class="lineno"> 326</span> col_scaling_vec << 0, 1, 2, -1;</div>
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<div class="line"><a id="l00327" name="l00327"></a><span class="lineno"> 327</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a54ded6625965f8ddd342161a55263cce">LInfRuizRescaling</a>(lp, <span class="comment">/*num_iterations=*/</span>1, row_scaling_vec, col_scaling_vec);</div>
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<div class="line"><a id="l00328" name="l00328"></a><span class="lineno"> 328</span> EXPECT_THAT(row_scaling_vec, ElementsAre(1 / std::sqrt(2), 1.0, 1.0, 1.0));</div>
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<div class="line"><a id="l00329" name="l00329"></a><span class="lineno"> 329</span> EXPECT_THAT(col_scaling_vec,</div>
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<div class="line"><a id="l00330" name="l00330"></a><span class="lineno"> 330</span> ElementsAre(0.0, 1.0, 2.0 / 3.0, -1.0 / std::sqrt(3.0)));</div>
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<div class="line"><a id="l00331" name="l00331"></a><span class="lineno"> 331</span>}</div>
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<div class="line"><a id="l00332" name="l00332"></a><span class="lineno"> 332</span> </div>
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<div class="line"><a id="l00333" name="l00333"></a><span class="lineno"> 333</span><span class="comment">// The test_lp matrix is [ 2 1 1 2; 1 0 1 0; 4 0 0 0; 0 0 1.5 -1],</span></div>
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<div class="line"><a id="l00334" name="l00334"></a><span class="lineno"> 334</span><span class="comment">// the scaled matrix is [ 0 1 2 -2; 0 0 4 0; 0 0 0 0; 0 0 9 3],</span></div>
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<div class="line"><a id="l00335" name="l00335"></a><span class="lineno"> 335</span><span class="comment">// so the row L2 norms are [3 4 0 sqrt(90)] and the column L2 norms are [0 1</span></div>
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<div class="line"><a id="l00336" name="l00336"></a><span class="lineno"> 336</span><span class="comment">// sqrt(101) sqrt(13)]. Rescaling divides the scaling vectors by sqrt(norms).</span></div>
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<div class="line"><a id="l00337" name="l00337"></a><span class="lineno"> 337</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(L2RuizRescaling, OneIteration) {</div>
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<div class="line"><a id="l00338" name="l00338"></a><span class="lineno"> 338</span> ShardedQuadraticProgram lp(<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ad795f23e2a85f4ae05efa1e2d0e0de4d">TestLp</a>(), <span class="comment">/*num_threads=*/</span>2, <span class="comment">/*num_shards=*/</span>2);</div>
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<div class="line"><a id="l00339" name="l00339"></a><span class="lineno"> 339</span> VectorXd row_scaling_vec(4), col_scaling_vec(4);</div>
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<div class="line"><a id="l00340" name="l00340"></a><span class="lineno"> 340</span> row_scaling_vec << 1, 2, 1, 3;</div>
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<div class="line"><a id="l00341" name="l00341"></a><span class="lineno"> 341</span> col_scaling_vec << 0, 1, 2, -1;</div>
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<div class="line"><a id="l00342" name="l00342"></a><span class="lineno"> 342</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a9dee2894b8028a57b7f7d2306b402e44">L2NormRescaling</a>(lp, row_scaling_vec, col_scaling_vec);</div>
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<div class="line"><a id="l00343" name="l00343"></a><span class="lineno"> 343</span> EXPECT_THAT(row_scaling_vec, ElementsAre(1.0 / std::pow(3.0, 0.5), 1.0, 1.0,</div>
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<div class="line"><a id="l00344" name="l00344"></a><span class="lineno"> 344</span> 3.0 / std::pow(90.0, 0.25)));</div>
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<div class="line"><a id="l00345" name="l00345"></a><span class="lineno"> 345</span> EXPECT_THAT(col_scaling_vec, ElementsAre(0.0, 1.0, 2.0 / std::pow(101, 0.25),</div>
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<div class="line"><a id="l00346" name="l00346"></a><span class="lineno"> 346</span> -1.0 / std::pow(13.0, 0.25)));</div>
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<div class="line"><a id="l00347" name="l00347"></a><span class="lineno"> 347</span>}</div>
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<div class="line"><a id="l00348" name="l00348"></a><span class="lineno"> 348</span> </div>
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<div class="line"><a id="l00349" name="l00349"></a><span class="lineno"> 349</span><span class="comment">// The test matrix is [2 3], so the row L2 norms are [sqrt(13)] and the column</span></div>
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<div class="line"><a id="l00350" name="l00350"></a><span class="lineno"> 350</span><span class="comment">// L2 norms are [2 3]. Rescaling divides the scaling vectors by sqrt(norms).</span></div>
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<div class="line"><a id="l00351" name="l00351"></a><span class="lineno"> 351</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(L2RuizRescaling, OneIterationNonSquare) {</div>
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<div class="line"><a id="l00352" name="l00352"></a><span class="lineno"> 352</span> QuadraticProgram test_lp(<span class="comment">/*num_variables=*/</span>2, <span class="comment">/*num_constraints=*/</span>1);</div>
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<div class="line"><a id="l00353" name="l00353"></a><span class="lineno"> 353</span> std::vector<Eigen::Triplet<double, int64_t>> triplets = {{0, 0, 2.0},</div>
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<div class="line"><a id="l00354" name="l00354"></a><span class="lineno"> 354</span> {0, 1, 3.0}};</div>
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<div class="line"><a id="l00355" name="l00355"></a><span class="lineno"> 355</span> test_lp.constraint_matrix.setFromTriplets(triplets.begin(), triplets.end());</div>
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<div class="line"><a id="l00356" name="l00356"></a><span class="lineno"> 356</span> ShardedQuadraticProgram lp(std::move(test_lp), <span class="comment">/*num_threads=*/</span>2,</div>
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<div class="line"><a id="l00357" name="l00357"></a><span class="lineno"> 357</span> <span class="comment">/*num_shards=*/</span>2);</div>
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<div class="line"><a id="l00358" name="l00358"></a><span class="lineno"> 358</span> VectorXd row_scaling_vec = VectorXd::Ones(1);</div>
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<div class="line"><a id="l00359" name="l00359"></a><span class="lineno"> 359</span> VectorXd col_scaling_vec = VectorXd::Ones(2);</div>
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<div class="line"><a id="l00360" name="l00360"></a><span class="lineno"> 360</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a9dee2894b8028a57b7f7d2306b402e44">L2NormRescaling</a>(lp, row_scaling_vec, col_scaling_vec);</div>
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<div class="line"><a id="l00361" name="l00361"></a><span class="lineno"> 361</span> EXPECT_THAT(row_scaling_vec, ElementsAre(1.0 / std::pow(13.0, 0.25)));</div>
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<div class="line"><a id="l00362" name="l00362"></a><span class="lineno"> 362</span> EXPECT_THAT(col_scaling_vec,</div>
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<div class="line"><a id="l00363" name="l00363"></a><span class="lineno"> 363</span> ElementsAre(1.0 / std::sqrt(2.0), 1.0 / std::sqrt(3.0)));</div>
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<div class="line"><a id="l00364" name="l00364"></a><span class="lineno"> 364</span>}</div>
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<div class="line"><a id="l00365" name="l00365"></a><span class="lineno"> 365</span> </div>
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<div class="line"><a id="l00366" name="l00366"></a><span class="lineno"> 366</span><span class="comment">// With many iterations of LInfRuizRescaling, the scaled matrix should converge</span></div>
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<div class="line"><a id="l00367" name="l00367"></a><span class="lineno"> 367</span><span class="comment">// to have col LInf norm 1 and row LInf norm 1.</span></div>
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<div class="line"><a id="l00368" name="l00368"></a><span class="lineno"> 368</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a54ded6625965f8ddd342161a55263cce">LInfRuizRescaling</a>, Convergence) {</div>
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<div class="line"><a id="l00369" name="l00369"></a><span class="lineno"> 369</span> ShardedQuadraticProgram lp(<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ad795f23e2a85f4ae05efa1e2d0e0de4d">TestLp</a>(), <span class="comment">/*num_threads=*/</span>2, <span class="comment">/*num_shards=*/</span>2);</div>
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<div class="line"><a id="l00370" name="l00370"></a><span class="lineno"> 370</span> VectorXd row_scaling_vec(4), col_scaling_vec(4);</div>
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<div class="line"><a id="l00371" name="l00371"></a><span class="lineno"> 371</span> VectorXd col_norm(4), row_norm(4);</div>
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<div class="line"><a id="l00372" name="l00372"></a><span class="lineno"> 372</span> row_scaling_vec << 1, 1, 1, 1;</div>
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<div class="line"><a id="l00373" name="l00373"></a><span class="lineno"> 373</span> col_scaling_vec << 1, 1, 1, 1;</div>
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<div class="line"><a id="l00374" name="l00374"></a><span class="lineno"> 374</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a54ded6625965f8ddd342161a55263cce">LInfRuizRescaling</a>(lp, <span class="comment">/*num_iterations=*/</span>20, row_scaling_vec,</div>
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<div class="line"><a id="l00375" name="l00375"></a><span class="lineno"> 375</span> col_scaling_vec);</div>
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<div class="line"><a id="l00376" name="l00376"></a><span class="lineno"> 376</span> col_norm = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a69a3cf251337531692721a574033a9df">ScaledColLInfNorm</a>(lp.Qp().constraint_matrix, row_scaling_vec,</div>
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<div class="line"><a id="l00377" name="l00377"></a><span class="lineno"> 377</span> col_scaling_vec, lp.ConstraintMatrixSharder());</div>
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<div class="line"><a id="l00378" name="l00378"></a><span class="lineno"> 378</span> row_norm = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a69a3cf251337531692721a574033a9df">ScaledColLInfNorm</a>(lp.TransposedConstraintMatrix(), col_scaling_vec,</div>
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<div class="line"><a id="l00379" name="l00379"></a><span class="lineno"> 379</span> row_scaling_vec,</div>
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<div class="line"><a id="l00380" name="l00380"></a><span class="lineno"> 380</span> lp.TransposedConstraintMatrixSharder());</div>
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<div class="line"><a id="l00381" name="l00381"></a><span class="lineno"> 381</span> EXPECT_THAT(row_norm, EigenArrayNear<double>({1.0, 1.0, 1.0, 1.0}, 1.0e-4));</div>
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<div class="line"><a id="l00382" name="l00382"></a><span class="lineno"> 382</span> EXPECT_THAT(col_norm, EigenArrayNear<double>({1.0, 1.0, 1.0, 1.0}, 1.0e-4));</div>
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<div class="line"><a id="l00383" name="l00383"></a><span class="lineno"> 383</span>}</div>
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<div class="line"><a id="l00384" name="l00384"></a><span class="lineno"> 384</span> </div>
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<div class="line"><a id="l00385" name="l00385"></a><span class="lineno"> 385</span><span class="comment">// This applies one round of l_inf and one round of L2 rescaling.</span></div>
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<div class="line"><a id="l00386" name="l00386"></a><span class="lineno"> 386</span><span class="comment">// The test_lp matrix is [ 2 1 1 2; 1 0 1 0; 4 0 0 0; 0 0 1.5 -1],</span></div>
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<div class="line"><a id="l00387" name="l00387"></a><span class="lineno"> 387</span><span class="comment">// so the row LInf norms are [2 1 4 1.5] and column LInf norms are [4 1 1.5 2].</span></div>
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<div class="line"><a id="l00388" name="l00388"></a><span class="lineno"> 388</span><span class="comment">// l_inf divides by sqrt(norms), giving</span></div>
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<div class="line"><a id="l00389" name="l00389"></a><span class="lineno"> 389</span><span class="comment">// [0.7071 0.7071 0.5773 1; 0.5 0 0.8165 0; 1 0 0 0; 0 0 1 -0.5773]</span></div>
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<div class="line"><a id="l00390" name="l00390"></a><span class="lineno"> 390</span><span class="comment">// which has row L2 norms [1.5275 0.957429 1 1.1547] and col L2 norms</span></div>
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<div class="line"><a id="l00391" name="l00391"></a><span class="lineno"> 391</span><span class="comment">// [1.3229 0.7071 1.4142 1.1547]. The resulting scaling vectors are</span></div>
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<div class="line"><a id="l00392" name="l00392"></a><span class="lineno"> 392</span><span class="comment">// 1/sqrt((l_inf norms).*(l2 norms)).</span></div>
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<div class="line"><a id="l00393" name="l00393"></a><span class="lineno"> 393</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#aac68304831a1bc81557fb03623a619d6">ApplyRescaling</a>, ApplyRescalingWorksForTestLp) {</div>
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<div class="line"><a id="l00394" name="l00394"></a><span class="lineno"> 394</span> ShardedQuadraticProgram lp(<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ad795f23e2a85f4ae05efa1e2d0e0de4d">TestLp</a>(), <span class="comment">/*num_threads=*/</span>2, <span class="comment">/*num_shards=*/</span>2);</div>
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<div class="line"><a id="l00395" name="l00395"></a><span class="lineno"> 395</span> ScalingVectors scaling = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#aac68304831a1bc81557fb03623a619d6">ApplyRescaling</a>(</div>
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<div class="line"><a id="l00396" name="l00396"></a><span class="lineno"> 396</span> RescalingOptions{.l_inf_ruiz_iterations = 1, .l2_norm_rescaling = <span class="keyword">true</span>},</div>
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<div class="line"><a id="l00397" name="l00397"></a><span class="lineno"> 397</span> lp);</div>
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<div class="line"><a id="l00398" name="l00398"></a><span class="lineno"> 398</span> EXPECT_THAT(scaling.row_scaling_vec,</div>
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<div class="line"><a id="l00399" name="l00399"></a><span class="lineno"> 399</span> EigenArrayNear<double>(</div>
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<div class="line"><a id="l00400" name="l00400"></a><span class="lineno"> 400</span> {1.0 / sqrt(2.0 * 1.5275), 1.0 / sqrt(1.0 * 0.9574),</div>
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<div class="line"><a id="l00401" name="l00401"></a><span class="lineno"> 401</span> 1.0 / sqrt(4.0 * 1.0), 1.0 / sqrt(1.5 * 1.1547)},</div>
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<div class="line"><a id="l00402" name="l00402"></a><span class="lineno"> 402</span> 1.0e-4));</div>
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<div class="line"><a id="l00403" name="l00403"></a><span class="lineno"> 403</span> EXPECT_THAT(scaling.col_scaling_vec,</div>
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<div class="line"><a id="l00404" name="l00404"></a><span class="lineno"> 404</span> EigenArrayNear<double>(</div>
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<div class="line"><a id="l00405" name="l00405"></a><span class="lineno"> 405</span> {1.0 / sqrt(4.0 * 1.3229), 1.0 / sqrt(1.0 * 0.7071),</div>
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<div class="line"><a id="l00406" name="l00406"></a><span class="lineno"> 406</span> 1.0 / sqrt(1.5 * 1.4142), 1.0 / sqrt(2.0 * 1.1547)},</div>
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<div class="line"><a id="l00407" name="l00407"></a><span class="lineno"> 407</span> 1.0e-4));</div>
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<div class="line"><a id="l00408" name="l00408"></a><span class="lineno"> 408</span>}</div>
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<div class="line"><a id="l00409" name="l00409"></a><span class="lineno"> 409</span> </div>
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<div class="line"><a id="l00410" name="l00410"></a><span class="lineno"> 410</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(ComputePrimalGradientTest, CorrectForLp) {</div>
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<div class="line"><a id="l00411" name="l00411"></a><span class="lineno"> 411</span> <span class="comment">// The choice of two shards is intentional, to help catch bugs in the sharded</span></div>
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<div class="line"><a id="l00412" name="l00412"></a><span class="lineno"> 412</span> <span class="comment">// computations.</span></div>
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<div class="line"><a id="l00413" name="l00413"></a><span class="lineno"> 413</span> ShardedQuadraticProgram lp(<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ad795f23e2a85f4ae05efa1e2d0e0de4d">TestLp</a>(), <span class="comment">/*num_threads=*/</span>2, <span class="comment">/*num_shards=*/</span>2);</div>
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<div class="line"><a id="l00414" name="l00414"></a><span class="lineno"> 414</span> </div>
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<div class="line"><a id="l00415" name="l00415"></a><span class="lineno"> 415</span> VectorXd primal_solution(4), dual_solution(4);</div>
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<div class="line"><a id="l00416" name="l00416"></a><span class="lineno"> 416</span> primal_solution << 0.0, 0.0, 0.0, 3.0;</div>
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<div class="line"><a id="l00417" name="l00417"></a><span class="lineno"> 417</span> dual_solution << -1.0, 0.0, 1.0, 1.0;</div>
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<div class="line"><a id="l00418" name="l00418"></a><span class="lineno"> 418</span> </div>
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<div class="line"><a id="l00419" name="l00419"></a><span class="lineno"> 419</span> <span class="keyword">const</span> LagrangianPart primal_part = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a259d3f73717a2ababa9df2dd43914656">ComputePrimalGradient</a>(</div>
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<div class="line"><a id="l00420" name="l00420"></a><span class="lineno"> 420</span> lp, primal_solution, lp.TransposedConstraintMatrix() * dual_solution);</div>
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<div class="line"><a id="l00421" name="l00421"></a><span class="lineno"> 421</span> <span class="comment">// Using notation consistent with</span></div>
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<div class="line"><a id="l00422" name="l00422"></a><span class="lineno"> 422</span> <span class="comment">// https://developers.google.com/optimization/lp/pdlp_math.</span></div>
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<div class="line"><a id="l00423" name="l00423"></a><span class="lineno"> 423</span> <span class="comment">// c - A'y</span></div>
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<div class="line"><a id="l00424" name="l00424"></a><span class="lineno"> 424</span> EXPECT_THAT(primal_part.gradient,</div>
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<div class="line"><a id="l00425" name="l00425"></a><span class="lineno"> 425</span> ElementsAre(5.5 - 2.0, -2.0 + 1.0, -1.0 - 0.5, 1.0 + 3.0));</div>
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<div class="line"><a id="l00426" name="l00426"></a><span class="lineno"> 426</span> <span class="comment">// c'x - y'Ax.</span></div>
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<div class="line"><a id="l00427" name="l00427"></a><span class="lineno"> 427</span> EXPECT_DOUBLE_EQ(primal_part.value, 3.0 + 9.0);</div>
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<div class="line"><a id="l00428" name="l00428"></a><span class="lineno"> 428</span>}</div>
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<div class="line"><a id="l00429" name="l00429"></a><span class="lineno"> 429</span> </div>
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<div class="line"><a id="l00430" name="l00430"></a><span class="lineno"> 430</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(ComputeDualGradientTest, CorrectForLp) {</div>
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<div class="line"><a id="l00431" name="l00431"></a><span class="lineno"> 431</span> <span class="comment">// The choice of two shards is intentional, to help catch bugs in the sharded</span></div>
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<div class="line"><a id="l00432" name="l00432"></a><span class="lineno"> 432</span> <span class="comment">// computations.</span></div>
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<div class="line"><a id="l00433" name="l00433"></a><span class="lineno"> 433</span> ShardedQuadraticProgram lp(<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ad795f23e2a85f4ae05efa1e2d0e0de4d">TestLp</a>(), <span class="comment">/*num_threads=*/</span>2, <span class="comment">/*num_shards=*/</span>2);</div>
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<div class="line"><a id="l00434" name="l00434"></a><span class="lineno"> 434</span> </div>
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<div class="line"><a id="l00435" name="l00435"></a><span class="lineno"> 435</span> VectorXd primal_solution(4), dual_solution(4);</div>
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<div class="line"><a id="l00436" name="l00436"></a><span class="lineno"> 436</span> primal_solution << 0.0, 0.0, 0.0, 3.0;</div>
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<div class="line"><a id="l00437" name="l00437"></a><span class="lineno"> 437</span> dual_solution << -1.0, 0.0, 1.0, 1.0;</div>
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<div class="line"><a id="l00438" name="l00438"></a><span class="lineno"> 438</span> </div>
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<div class="line"><a id="l00439" name="l00439"></a><span class="lineno"> 439</span> <span class="keyword">const</span> LagrangianPart dual_part = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a608ed26a4c7ff3bdcb22d25ff890f47d">ComputeDualGradient</a>(</div>
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<div class="line"><a id="l00440" name="l00440"></a><span class="lineno"> 440</span> lp, dual_solution, lp.Qp().constraint_matrix * primal_solution);</div>
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<div class="line"><a id="l00441" name="l00441"></a><span class="lineno"> 441</span> <span class="comment">// Using notation consistent with</span></div>
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<div class="line"><a id="l00442" name="l00442"></a><span class="lineno"> 442</span> <span class="comment">// https://developers.google.com/optimization/lp/pdlp_math.</span></div>
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<div class="line"><a id="l00443" name="l00443"></a><span class="lineno"> 443</span> <span class="comment">// active_constraint_right_hand_side - Ax</span></div>
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<div class="line"><a id="l00444" name="l00444"></a><span class="lineno"> 444</span> EXPECT_THAT(dual_part.gradient,</div>
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<div class="line"><a id="l00445" name="l00445"></a><span class="lineno"> 445</span> ElementsAre(12.0 - 6.0, 7.0, -4.0, -1.0 + 3.0));</div>
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<div class="line"><a id="l00446" name="l00446"></a><span class="lineno"> 446</span> <span class="comment">// y'active_constraint_right_hand_side</span></div>
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<div class="line"><a id="l00447" name="l00447"></a><span class="lineno"> 447</span> EXPECT_DOUBLE_EQ(dual_part.value, 12.0 * -1.0 + -4.0 * 1.0 + -1.0 * 1.0);</div>
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<div class="line"><a id="l00448" name="l00448"></a><span class="lineno"> 448</span>}</div>
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<div class="line"><a id="l00449" name="l00449"></a><span class="lineno"> 449</span> </div>
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<div class="line"><a id="l00450" name="l00450"></a><span class="lineno"> 450</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(ComputeDualGradientTest, CorrectOnTwoSidedConstraints) {</div>
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<div class="line"><a id="l00451" name="l00451"></a><span class="lineno"> 451</span> QuadraticProgram qp = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ad795f23e2a85f4ae05efa1e2d0e0de4d">TestLp</a>();</div>
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<div class="line"><a id="l00452" name="l00452"></a><span class="lineno"> 452</span> <span class="comment">// Makes the constraints all two-sided. The primal solution is feasible in</span></div>
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<div class="line"><a id="l00453" name="l00453"></a><span class="lineno"> 453</span> <span class="comment">// the first constraint, below the lower bound of the second constraint, and</span></div>
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<div class="line"><a id="l00454" name="l00454"></a><span class="lineno"> 454</span> <span class="comment">// above the upper bound of the third constraint.</span></div>
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<div class="line"><a id="l00455" name="l00455"></a><span class="lineno"> 455</span> qp.constraint_lower_bounds[0] = 4;</div>
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<div class="line"><a id="l00456" name="l00456"></a><span class="lineno"> 456</span> qp.constraint_lower_bounds[1] = 5;</div>
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<div class="line"><a id="l00457" name="l00457"></a><span class="lineno"> 457</span> qp.constraint_upper_bounds[2] = -1;</div>
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<div class="line"><a id="l00458" name="l00458"></a><span class="lineno"> 458</span> ShardedQuadraticProgram sharded_qp(std::move(qp), <span class="comment">/*num_threads=*/</span>2,</div>
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<div class="line"><a id="l00459" name="l00459"></a><span class="lineno"> 459</span> <span class="comment">/*num_shards=*/</span>2);</div>
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<div class="line"><a id="l00460" name="l00460"></a><span class="lineno"> 460</span> </div>
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<div class="line"><a id="l00461" name="l00461"></a><span class="lineno"> 461</span> VectorXd primal_solution(4), dual_solution(4);</div>
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<div class="line"><a id="l00462" name="l00462"></a><span class="lineno"> 462</span> primal_solution << 0.0, 0.0, 0.0, 3.0;</div>
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<div class="line"><a id="l00463" name="l00463"></a><span class="lineno"> 463</span> dual_solution << 0.0, 0.0, 0.0, -1.0;</div>
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<div class="line"><a id="l00464" name="l00464"></a><span class="lineno"> 464</span> </div>
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<div class="line"><a id="l00465" name="l00465"></a><span class="lineno"> 465</span> <span class="keyword">const</span> LagrangianPart dual_part =</div>
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<div class="line"><a id="l00466" name="l00466"></a><span class="lineno"> 466</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a608ed26a4c7ff3bdcb22d25ff890f47d">ComputeDualGradient</a>(sharded_qp, dual_solution,</div>
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<div class="line"><a id="l00467" name="l00467"></a><span class="lineno"> 467</span> sharded_qp.Qp().constraint_matrix * primal_solution);</div>
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<div class="line"><a id="l00468" name="l00468"></a><span class="lineno"> 468</span> <span class="comment">// Using notation consistent with</span></div>
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<div class="line"><a id="l00469" name="l00469"></a><span class="lineno"> 469</span> <span class="comment">// https://developers.google.com/optimization/lp/pdlp_math.</span></div>
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<div class="line"><a id="l00470" name="l00470"></a><span class="lineno"> 470</span> <span class="comment">// active_constraint_right_hand_side - Ax</span></div>
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<div class="line"><a id="l00471" name="l00471"></a><span class="lineno"> 471</span> EXPECT_THAT(dual_part.gradient,</div>
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<div class="line"><a id="l00472" name="l00472"></a><span class="lineno"> 472</span> ElementsAre(0.0, 5.0 - 0.0, -1.0 - 0.0, 1.0 + 3.0));</div>
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<div class="line"><a id="l00473" name="l00473"></a><span class="lineno"> 473</span> <span class="comment">// y'active_constraint_right_hand_side</span></div>
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<div class="line"><a id="l00474" name="l00474"></a><span class="lineno"> 474</span> EXPECT_DOUBLE_EQ(dual_part.value, 1.0 * -1.0);</div>
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|
<div class="line"><a id="l00475" name="l00475"></a><span class="lineno"> 475</span>}</div>
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<div class="line"><a id="l00476" name="l00476"></a><span class="lineno"> 476</span> </div>
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<div class="line"><a id="l00477" name="l00477"></a><span class="lineno"> 477</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(HasValidBoundsTest, SmallInvalidLp) {</div>
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<div class="line"><a id="l00478" name="l00478"></a><span class="lineno"> 478</span> ShardedQuadraticProgram lp(<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a90ad9eef4ca03500a77b99e9e29a675f">SmallInvalidProblemLp</a>(), <span class="comment">/*num_threads=*/</span>2,</div>
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<div class="line"><a id="l00479" name="l00479"></a><span class="lineno"> 479</span> <span class="comment">/*num_shards=*/</span>2);</div>
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<div class="line"><a id="l00480" name="l00480"></a><span class="lineno"> 480</span> </div>
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<div class="line"><a id="l00481" name="l00481"></a><span class="lineno"> 481</span> <span class="keywordtype">bool</span> is_valid = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a77dbe245ed9fb597ad836b27ac989f26">HasValidBounds</a>(lp);</div>
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<div class="line"><a id="l00482" name="l00482"></a><span class="lineno"> 482</span> EXPECT_FALSE(is_valid);</div>
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<div class="line"><a id="l00483" name="l00483"></a><span class="lineno"> 483</span>}</div>
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<div class="line"><a id="l00484" name="l00484"></a><span class="lineno"> 484</span> </div>
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<div class="line"><a id="l00485" name="l00485"></a><span class="lineno"> 485</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(HasValidBoundsTest, SmallValidLp) {</div>
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<div class="line"><a id="l00486" name="l00486"></a><span class="lineno"> 486</span> ShardedQuadraticProgram lp(<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#af2390d8030d8925da948d466b7075d39">SmallPrimalInfeasibleLp</a>(), <span class="comment">/*num_threads=*/</span>2,</div>
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<div class="line"><a id="l00487" name="l00487"></a><span class="lineno"> 487</span> <span class="comment">/*num_shards=*/</span>2);</div>
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<div class="line"><a id="l00488" name="l00488"></a><span class="lineno"> 488</span> </div>
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<div class="line"><a id="l00489" name="l00489"></a><span class="lineno"> 489</span> <span class="keywordtype">bool</span> is_valid = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a77dbe245ed9fb597ad836b27ac989f26">HasValidBounds</a>(lp);</div>
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<div class="line"><a id="l00490" name="l00490"></a><span class="lineno"> 490</span> EXPECT_TRUE(is_valid);</div>
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<div class="line"><a id="l00491" name="l00491"></a><span class="lineno"> 491</span>}</div>
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<div class="line"><a id="l00492" name="l00492"></a><span class="lineno"> 492</span> </div>
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<div class="line"><a id="l00493" name="l00493"></a><span class="lineno"> 493</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(ComputePrimalGradientTest, CorrectForQp) {</div>
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<div class="line"><a id="l00494" name="l00494"></a><span class="lineno"> 494</span> ShardedQuadraticProgram qp(<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#afaedaf1e3ebe4d6d1a36e3fd1f206de6">TestDiagonalQp1</a>(), <span class="comment">/*num_threads=*/</span>2,</div>
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<div class="line"><a id="l00495" name="l00495"></a><span class="lineno"> 495</span> <span class="comment">/*num_shards=*/</span>2);</div>
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<div class="line"><a id="l00496" name="l00496"></a><span class="lineno"> 496</span> </div>
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<div class="line"><a id="l00497" name="l00497"></a><span class="lineno"> 497</span> VectorXd primal_solution(2), dual_solution(1);</div>
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<div class="line"><a id="l00498" name="l00498"></a><span class="lineno"> 498</span> primal_solution << 1.0, 2.0;</div>
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<div class="line"><a id="l00499" name="l00499"></a><span class="lineno"> 499</span> dual_solution << -2.0;</div>
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<div class="line"><a id="l00500" name="l00500"></a><span class="lineno"> 500</span> </div>
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<div class="line"><a id="l00501" name="l00501"></a><span class="lineno"> 501</span> <span class="keyword">const</span> LagrangianPart primal_part = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a259d3f73717a2ababa9df2dd43914656">ComputePrimalGradient</a>(</div>
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<div class="line"><a id="l00502" name="l00502"></a><span class="lineno"> 502</span> qp, primal_solution, qp.TransposedConstraintMatrix() * dual_solution);</div>
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<div class="line"><a id="l00503" name="l00503"></a><span class="lineno"> 503</span> </div>
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<div class="line"><a id="l00504" name="l00504"></a><span class="lineno"> 504</span> <span class="comment">// Using notation consistent with</span></div>
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<div class="line"><a id="l00505" name="l00505"></a><span class="lineno"> 505</span> <span class="comment">// https://developers.google.com/optimization/lp/pdlp_math.</span></div>
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<div class="line"><a id="l00506" name="l00506"></a><span class="lineno"> 506</span> <span class="comment">// c - A'y + Qx</span></div>
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<div class="line"><a id="l00507" name="l00507"></a><span class="lineno"> 507</span> EXPECT_THAT(primal_part.gradient,</div>
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<div class="line"><a id="l00508" name="l00508"></a><span class="lineno"> 508</span> ElementsAre(-1.0 + 2.0 + 4.0, -1.0 + 2.0 + 2.0));</div>
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<div class="line"><a id="l00509" name="l00509"></a><span class="lineno"> 509</span> <span class="comment">// (1/2) x'Qx + c'x - y'Ax.</span></div>
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<div class="line"><a id="l00510" name="l00510"></a><span class="lineno"> 510</span> EXPECT_DOUBLE_EQ(primal_part.value, 4.0 - 3.0 + 2.0 * 3.0);</div>
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<div class="line"><a id="l00511" name="l00511"></a><span class="lineno"> 511</span>}</div>
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<div class="line"><a id="l00512" name="l00512"></a><span class="lineno"> 512</span> </div>
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<div class="line"><a id="l00513" name="l00513"></a><span class="lineno"> 513</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(ComputeDualGradientTest, CorrectForQp) {</div>
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<div class="line"><a id="l00514" name="l00514"></a><span class="lineno"> 514</span> ShardedQuadraticProgram qp(<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#afaedaf1e3ebe4d6d1a36e3fd1f206de6">TestDiagonalQp1</a>(), <span class="comment">/*num_threads=*/</span>2,</div>
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<div class="line"><a id="l00515" name="l00515"></a><span class="lineno"> 515</span> <span class="comment">/*num_shards=*/</span>2);</div>
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<div class="line"><a id="l00516" name="l00516"></a><span class="lineno"> 516</span> </div>
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<div class="line"><a id="l00517" name="l00517"></a><span class="lineno"> 517</span> VectorXd primal_solution(2), dual_solution(1);</div>
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<div class="line"><a id="l00518" name="l00518"></a><span class="lineno"> 518</span> primal_solution << 1.0, 2.0;</div>
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<div class="line"><a id="l00519" name="l00519"></a><span class="lineno"> 519</span> dual_solution << -2.0;</div>
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<div class="line"><a id="l00520" name="l00520"></a><span class="lineno"> 520</span> </div>
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<div class="line"><a id="l00521" name="l00521"></a><span class="lineno"> 521</span> <span class="keyword">const</span> LagrangianPart dual_part = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a608ed26a4c7ff3bdcb22d25ff890f47d">ComputeDualGradient</a>(</div>
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<div class="line"><a id="l00522" name="l00522"></a><span class="lineno"> 522</span> qp, dual_solution, qp.Qp().constraint_matrix * primal_solution);</div>
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<div class="line"><a id="l00523" name="l00523"></a><span class="lineno"> 523</span> </div>
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<div class="line"><a id="l00524" name="l00524"></a><span class="lineno"> 524</span> <span class="comment">// Using notation consistent with</span></div>
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<div class="line"><a id="l00525" name="l00525"></a><span class="lineno"> 525</span> <span class="comment">// https://developers.google.com/optimization/lp/pdlp_math.</span></div>
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<div class="line"><a id="l00526" name="l00526"></a><span class="lineno"> 526</span> <span class="comment">// active_constraint_right_hand_side - Ax</span></div>
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<div class="line"><a id="l00527" name="l00527"></a><span class="lineno"> 527</span> EXPECT_THAT(dual_part.gradient, ElementsAre(1.0 - (1.0 + 2.0)));</div>
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<div class="line"><a id="l00528" name="l00528"></a><span class="lineno"> 528</span> <span class="comment">// y'active_constraint_right_hand_side</span></div>
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<div class="line"><a id="l00529" name="l00529"></a><span class="lineno"> 529</span> EXPECT_DOUBLE_EQ(dual_part.value, -2.0);</div>
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<div class="line"><a id="l00530" name="l00530"></a><span class="lineno"> 530</span>}</div>
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<div class="line"><a id="l00531" name="l00531"></a><span class="lineno"> 531</span> </div>
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<div class="line"><a id="l00532" name="l00532"></a><span class="lineno"> 532</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(EstimateSingularValuesTest, CorrectForTestLp) {</div>
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<div class="line"><a id="l00533" name="l00533"></a><span class="lineno"> 533</span> ShardedQuadraticProgram lp(<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ad795f23e2a85f4ae05efa1e2d0e0de4d">TestLp</a>(), <span class="comment">/*num_threads=*/</span>2, <span class="comment">/*num_shards=*/</span>2);</div>
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<div class="line"><a id="l00534" name="l00534"></a><span class="lineno"> 534</span> </div>
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<div class="line"><a id="l00535" name="l00535"></a><span class="lineno"> 535</span> <span class="comment">// The test_lp matrix is [ 2 1 1 2; 1 0 1 0; 4 0 0 0; 0 0 1.5 -1].</span></div>
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<div class="line"><a id="l00536" name="l00536"></a><span class="lineno"> 536</span> std::mt19937 random(1);</div>
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<div class="line"><a id="l00537" name="l00537"></a><span class="lineno"> 537</span> <span class="keyword">auto</span> result = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a880902cb3a98b7205fa57be9e16a82c7">EstimateMaximumSingularValueOfConstraintMatrix</a>(</div>
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<div class="line"><a id="l00538" name="l00538"></a><span class="lineno"> 538</span> lp, absl::nullopt, absl::nullopt,</div>
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<div class="line"><a id="l00539" name="l00539"></a><span class="lineno"> 539</span> <span class="comment">/*desired_relative_error=*/</span>0.01,</div>
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<div class="line"><a id="l00540" name="l00540"></a><span class="lineno"> 540</span> <span class="comment">/*failure_probability=*/</span>0.001, random);</div>
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<div class="line"><a id="l00541" name="l00541"></a><span class="lineno"> 541</span> EXPECT_NEAR(result.singular_value, 4.76945, 0.01);</div>
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<div class="line"><a id="l00542" name="l00542"></a><span class="lineno"> 542</span> EXPECT_LT(result.num_iterations, 300);</div>
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<div class="line"><a id="l00543" name="l00543"></a><span class="lineno"> 543</span>}</div>
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<div class="line"><a id="l00544" name="l00544"></a><span class="lineno"> 544</span> </div>
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<div class="line"><a id="l00545" name="l00545"></a><span class="lineno"> 545</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(EstimateSingularValuesTest, CorrectForTestLpWithActivePrimalSubspace) {</div>
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<div class="line"><a id="l00546" name="l00546"></a><span class="lineno"> 546</span> ShardedQuadraticProgram lp(<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ad795f23e2a85f4ae05efa1e2d0e0de4d">TestLp</a>(), <span class="comment">/*num_threads=*/</span>2, <span class="comment">/*num_shards=*/</span>2);</div>
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<div class="line"><a id="l00547" name="l00547"></a><span class="lineno"> 547</span> </div>
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<div class="line"><a id="l00548" name="l00548"></a><span class="lineno"> 548</span> VectorXd primal_solution(4);</div>
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<div class="line"><a id="l00549" name="l00549"></a><span class="lineno"> 549</span> <span class="comment">// Chosen so x_1 is at its bound, and all other variables are not at bounds.</span></div>
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<div class="line"><a id="l00550" name="l00550"></a><span class="lineno"> 550</span> primal_solution << 0.0, -2.0, 0.0, 3.0;</div>
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<div class="line"><a id="l00551" name="l00551"></a><span class="lineno"> 551</span> <span class="comment">// The test_lp matrix is [ 2 1 1 2; 1 0 1 0; 4 0 0 0; 0 0 1.5 -1],</span></div>
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<div class="line"><a id="l00552" name="l00552"></a><span class="lineno"> 552</span> <span class="comment">// so the projected matrix is [ 2 1 2; 1 1 0; 4 0 0; 0 1.5 -1].</span></div>
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<div class="line"><a id="l00553" name="l00553"></a><span class="lineno"> 553</span> std::mt19937 random(1);</div>
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<div class="line"><a id="l00554" name="l00554"></a><span class="lineno"> 554</span> <span class="keyword">auto</span> result = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a880902cb3a98b7205fa57be9e16a82c7">EstimateMaximumSingularValueOfConstraintMatrix</a>(</div>
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<div class="line"><a id="l00555" name="l00555"></a><span class="lineno"> 555</span> lp, primal_solution, absl::nullopt, <span class="comment">/*desired_relative_error=*/</span>0.01,</div>
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<div class="line"><a id="l00556" name="l00556"></a><span class="lineno"> 556</span> <span class="comment">/*failure_probability=*/</span>0.001, random);</div>
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<div class="line"><a id="l00557" name="l00557"></a><span class="lineno"> 557</span> EXPECT_NEAR(result.singular_value, 4.73818, 0.01);</div>
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<div class="line"><a id="l00558" name="l00558"></a><span class="lineno"> 558</span> EXPECT_LT(result.num_iterations, 300);</div>
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<div class="line"><a id="l00559" name="l00559"></a><span class="lineno"> 559</span>}</div>
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<div class="line"><a id="l00560" name="l00560"></a><span class="lineno"> 560</span> </div>
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<div class="line"><a id="l00561" name="l00561"></a><span class="lineno"> 561</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(EstimateSingularValuesTest, CorrectForTestLpWithActiveDualSubspace) {</div>
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<div class="line"><a id="l00562" name="l00562"></a><span class="lineno"> 562</span> ShardedQuadraticProgram lp(<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ad795f23e2a85f4ae05efa1e2d0e0de4d">TestLp</a>(), <span class="comment">/*num_threads=*/</span>2, <span class="comment">/*num_shards=*/</span>2);</div>
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<div class="line"><a id="l00563" name="l00563"></a><span class="lineno"> 563</span> </div>
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<div class="line"><a id="l00564" name="l00564"></a><span class="lineno"> 564</span> VectorXd dual_solution(4);</div>
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<div class="line"><a id="l00565" name="l00565"></a><span class="lineno"> 565</span> <span class="comment">// Chosen so the second dual is at its bound, and all other duals are not at</span></div>
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<div class="line"><a id="l00566" name="l00566"></a><span class="lineno"> 566</span> <span class="comment">// bounds.</span></div>
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<div class="line"><a id="l00567" name="l00567"></a><span class="lineno"> 567</span> dual_solution << 1.0, 0.0, 1.0, 3.0;</div>
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<div class="line"><a id="l00568" name="l00568"></a><span class="lineno"> 568</span> <span class="comment">// The test_lp matrix is [ 2 1 1 2; 1 0 1 0; 4 0 0 0; 0 0 1.5 -1],</span></div>
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<div class="line"><a id="l00569" name="l00569"></a><span class="lineno"> 569</span> <span class="comment">// so the projected matrix is [ 2 1 1 2; 4 0 0 0; 0 0 1.5 -1].</span></div>
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<div class="line"><a id="l00570" name="l00570"></a><span class="lineno"> 570</span> std::mt19937 random(1);</div>
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<div class="line"><a id="l00571" name="l00571"></a><span class="lineno"> 571</span> <span class="keyword">auto</span> result = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a880902cb3a98b7205fa57be9e16a82c7">EstimateMaximumSingularValueOfConstraintMatrix</a>(</div>
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<div class="line"><a id="l00572" name="l00572"></a><span class="lineno"> 572</span> lp, absl::nullopt, dual_solution, <span class="comment">/*desired_relative_error=*/</span>0.01,</div>
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<div class="line"><a id="l00573" name="l00573"></a><span class="lineno"> 573</span> <span class="comment">/*failure_probability=*/</span>0.001, random);</div>
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<div class="line"><a id="l00574" name="l00574"></a><span class="lineno"> 574</span> EXPECT_NEAR(result.singular_value, 4.64203, 0.01);</div>
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<div class="line"><a id="l00575" name="l00575"></a><span class="lineno"> 575</span> EXPECT_LT(result.num_iterations, 300);</div>
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<div class="line"><a id="l00576" name="l00576"></a><span class="lineno"> 576</span>}</div>
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<div class="line"><a id="l00577" name="l00577"></a><span class="lineno"> 577</span> </div>
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<div class="line"><a id="l00578" name="l00578"></a><span class="lineno"> 578</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(EstimateSingularValuesTest, CorrectForTestLpWithBothActiveSubspaces) {</div>
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<div class="line"><a id="l00579" name="l00579"></a><span class="lineno"> 579</span> ShardedQuadraticProgram lp(<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ad795f23e2a85f4ae05efa1e2d0e0de4d">TestLp</a>(), <span class="comment">/*num_threads=*/</span>2, <span class="comment">/*num_shards=*/</span>2);</div>
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<div class="line"><a id="l00580" name="l00580"></a><span class="lineno"> 580</span> </div>
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<div class="line"><a id="l00581" name="l00581"></a><span class="lineno"> 581</span> VectorXd primal_solution(4), dual_solution(4);</div>
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<div class="line"><a id="l00582" name="l00582"></a><span class="lineno"> 582</span> <span class="comment">// Chosen so x_1 is at its bound, and all other variables are not at bounds.</span></div>
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<div class="line"><a id="l00583" name="l00583"></a><span class="lineno"> 583</span> primal_solution << 0.0, -2.0, 0.0, 3.0;</div>
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<div class="line"><a id="l00584" name="l00584"></a><span class="lineno"> 584</span> <span class="comment">// Chosen so the second dual is at its bound, and all other duals are not at</span></div>
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<div class="line"><a id="l00585" name="l00585"></a><span class="lineno"> 585</span> <span class="comment">// bounds.</span></div>
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<div class="line"><a id="l00586" name="l00586"></a><span class="lineno"> 586</span> dual_solution << 1.0, 0.0, 1.0, 3.0;</div>
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<div class="line"><a id="l00587" name="l00587"></a><span class="lineno"> 587</span> <span class="comment">// The test_lp matrix is [ 2 1 1 2; 1 0 1 0; 4 0 0 0; 0 0 1.5 -1],</span></div>
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<div class="line"><a id="l00588" name="l00588"></a><span class="lineno"> 588</span> <span class="comment">// so the projected matrix is [ 2 1 2; 4 0 0; 0 1.5 -1].</span></div>
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<div class="line"><a id="l00589" name="l00589"></a><span class="lineno"> 589</span> std::mt19937 random(1);</div>
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<div class="line"><a id="l00590" name="l00590"></a><span class="lineno"> 590</span> <span class="keyword">auto</span> result = <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a880902cb3a98b7205fa57be9e16a82c7">EstimateMaximumSingularValueOfConstraintMatrix</a>(</div>
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<div class="line"><a id="l00591" name="l00591"></a><span class="lineno"> 591</span> lp, primal_solution, dual_solution, <span class="comment">/*desired_relative_error=*/</span>0.01,</div>
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<div class="line"><a id="l00592" name="l00592"></a><span class="lineno"> 592</span> <span class="comment">/*failure_probability=*/</span>0.001, random);</div>
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<div class="line"><a id="l00593" name="l00593"></a><span class="lineno"> 593</span> EXPECT_NEAR(result.singular_value, 4.60829, 0.01);</div>
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<div class="line"><a id="l00594" name="l00594"></a><span class="lineno"> 594</span> EXPECT_LT(result.num_iterations, 300);</div>
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<div class="line"><a id="l00595" name="l00595"></a><span class="lineno"> 595</span>}</div>
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|
<div class="line"><a id="l00596" name="l00596"></a><span class="lineno"> 596</span> </div>
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<div class="line"><a id="l00597" name="l00597"></a><span class="lineno"> 597</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(ProjectToPrimalVariableBoundsTest, <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ad795f23e2a85f4ae05efa1e2d0e0de4d">TestLp</a>) {</div>
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|
<div class="line"><a id="l00598" name="l00598"></a><span class="lineno"> 598</span> ShardedQuadraticProgram qp(<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ad795f23e2a85f4ae05efa1e2d0e0de4d">TestLp</a>(), <span class="comment">/*num_threads=*/</span>2,</div>
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|
<div class="line"><a id="l00599" name="l00599"></a><span class="lineno"> 599</span> <span class="comment">/*num_shards=*/</span>2);</div>
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|
<div class="line"><a id="l00600" name="l00600"></a><span class="lineno"> 600</span> VectorXd primal(4);</div>
|
|
<div class="line"><a id="l00601" name="l00601"></a><span class="lineno"> 601</span> primal << -3, -3, 5, 5;</div>
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|
<div class="line"><a id="l00602" name="l00602"></a><span class="lineno"> 602</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#acb7f29f435d6c9fc53148ee403c7049e">ProjectToPrimalVariableBounds</a>(qp, primal);</div>
|
|
<div class="line"><a id="l00603" name="l00603"></a><span class="lineno"> 603</span> EXPECT_THAT(primal, ElementsAre(-3, -2, 5, 3.5));</div>
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|
<div class="line"><a id="l00604" name="l00604"></a><span class="lineno"> 604</span>}</div>
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|
<div class="line"><a id="l00605" name="l00605"></a><span class="lineno"> 605</span> </div>
|
|
<div class="line"><a id="l00606" name="l00606"></a><span class="lineno"> 606</span><a class="code hl_function" href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">TEST</a>(ProjectToDualVariableBoundsTest, <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ad795f23e2a85f4ae05efa1e2d0e0de4d">TestLp</a>) {</div>
|
|
<div class="line"><a id="l00607" name="l00607"></a><span class="lineno"> 607</span> ShardedQuadraticProgram qp(<a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#ad795f23e2a85f4ae05efa1e2d0e0de4d">TestLp</a>(), <span class="comment">/*num_threads=*/</span>2,</div>
|
|
<div class="line"><a id="l00608" name="l00608"></a><span class="lineno"> 608</span> <span class="comment">/*num_shards=*/</span>2);</div>
|
|
<div class="line"><a id="l00609" name="l00609"></a><span class="lineno"> 609</span> VectorXd dual(4);</div>
|
|
<div class="line"><a id="l00610" name="l00610"></a><span class="lineno"> 610</span> dual << 1, 1, -1, -1;</div>
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<div class="line"><a id="l00611" name="l00611"></a><span class="lineno"> 611</span> <a class="code hl_function" href="namespaceoperations__research_1_1pdlp.html#a898c0c776a5736cf1931036d0d370724">ProjectToDualVariableBounds</a>(qp, dual);</div>
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|
<div class="line"><a id="l00612" name="l00612"></a><span class="lineno"> 612</span> EXPECT_THAT(dual, ElementsAre(1, 0, 0, -1));</div>
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|
<div class="line"><a id="l00613" name="l00613"></a><span class="lineno"> 613</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">// namespace</span></div>
|
|
<div class="line"><a id="l00616" name="l00616"></a><span class="lineno"> 616</span>} <span class="comment">// namespace operations_research::pdlp</span></div>
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|
<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html">operations_research::pdlp</a></div><div class="ttdef"><b>Definition:</b> <a href="iteration__stats_8cc_source.html#l00040">iteration_stats.cc:40</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a259d3f73717a2ababa9df2dd43914656"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a259d3f73717a2ababa9df2dd43914656">operations_research::pdlp::ComputePrimalGradient</a></div><div class="ttdeci">LagrangianPart ComputePrimalGradient(const ShardedQuadraticProgram &sharded_qp, const VectorXd &primal_solution, const VectorXd &dual_product)</div><div class="ttdef"><b>Definition:</b> <a href="sharded__optimization__utils_8cc_source.html#l00462">sharded_optimization_utils.cc:462</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a54ded6625965f8ddd342161a55263cce"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a54ded6625965f8ddd342161a55263cce">operations_research::pdlp::LInfRuizRescaling</a></div><div class="ttdeci">void LInfRuizRescaling(const ShardedQuadraticProgram &sharded_qp, const int num_iterations, VectorXd &row_scaling_vec, VectorXd &col_scaling_vec)</div><div class="ttdef"><b>Definition:</b> <a href="sharded__optimization__utils_8cc_source.html#l00425">sharded_optimization_utils.cc:425</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a608ed26a4c7ff3bdcb22d25ff890f47d"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a608ed26a4c7ff3bdcb22d25ff890f47d">operations_research::pdlp::ComputeDualGradient</a></div><div class="ttdeci">LagrangianPart ComputeDualGradient(const ShardedQuadraticProgram &sharded_qp, const Eigen::VectorXd &dual_solution, const Eigen::VectorXd &primal_product)</div><div class="ttdef"><b>Definition:</b> <a href="sharded__optimization__utils_8cc_source.html#l00518">sharded_optimization_utils.cc:518</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a69a3cf251337531692721a574033a9df"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a69a3cf251337531692721a574033a9df">operations_research::pdlp::ScaledColLInfNorm</a></div><div class="ttdeci">VectorXd ScaledColLInfNorm(const Eigen::SparseMatrix< double, Eigen::ColMajor, int64_t > &matrix, const VectorXd &row_scaling_vec, const VectorXd &col_scaling_vec, const Sharder &sharder)</div><div class="ttdef"><b>Definition:</b> <a href="sharder_8cc_source.html#l00258">sharder.cc:258</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a77dbe245ed9fb597ad836b27ac989f26"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a77dbe245ed9fb597ad836b27ac989f26">operations_research::pdlp::HasValidBounds</a></div><div class="ttdeci">bool HasValidBounds(const QuadraticProgram &qp)</div><div class="ttdef"><b>Definition:</b> <a href="quadratic__program_8cc_source.html#l00084">quadratic_program.cc:84</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a79496743a139659305201925cdcb39fa"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a79496743a139659305201925cdcb39fa">operations_research::pdlp::TinyLp</a></div><div class="ttdeci">QuadraticProgram TinyLp()</div><div class="ttdef"><b>Definition:</b> <a href="test__util_8cc_source.html#l00066">test_util.cc:66</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a880902cb3a98b7205fa57be9e16a82c7"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a880902cb3a98b7205fa57be9e16a82c7">operations_research::pdlp::EstimateMaximumSingularValueOfConstraintMatrix</a></div><div class="ttdeci">SingularValueAndIterations EstimateMaximumSingularValueOfConstraintMatrix(const ShardedQuadraticProgram &sharded_qp, const absl::optional< VectorXd > &primal_solution, const absl::optional< VectorXd > &dual_solution, const double desired_relative_error, const double failure_probability, std::mt19937 &mt_generator)</div><div class="ttdef"><b>Definition:</b> <a href="sharded__optimization__utils_8cc_source.html#l00706">sharded_optimization_utils.cc:706</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a898c0c776a5736cf1931036d0d370724"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a898c0c776a5736cf1931036d0d370724">operations_research::pdlp::ProjectToDualVariableBounds</a></div><div class="ttdeci">void ProjectToDualVariableBounds(const ShardedQuadraticProgram &sharded_qp, VectorXd &dual)</div><div class="ttdef"><b>Definition:</b> <a href="sharded__optimization__utils_8cc_source.html#l00762">sharded_optimization_utils.cc:762</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a8d1b6834fa651b584ac83326d6a283b9"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a8d1b6834fa651b584ac83326d6a283b9">operations_research::pdlp::LpWithoutConstraints</a></div><div class="ttdeci">QuadraticProgram LpWithoutConstraints()</div><div class="ttdef"><b>Definition:</b> <a href="test__util_8cc_source.html#l00261">test_util.cc:261</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a90ad9eef4ca03500a77b99e9e29a675f"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a90ad9eef4ca03500a77b99e9e29a675f">operations_research::pdlp::SmallInvalidProblemLp</a></div><div class="ttdeci">QuadraticProgram SmallInvalidProblemLp()</div><div class="ttdef"><b>Definition:</b> <a href="test__util_8cc_source.html#l00190">test_util.cc:190</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_a9dee2894b8028a57b7f7d2306b402e44"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#a9dee2894b8028a57b7f7d2306b402e44">operations_research::pdlp::L2NormRescaling</a></div><div class="ttdeci">void L2NormRescaling(const ShardedQuadraticProgram &sharded_qp, VectorXd &row_scaling_vec, VectorXd &col_scaling_vec)</div><div class="ttdef"><b>Definition:</b> <a href="sharded__optimization__utils_8cc_source.html#l00432">sharded_optimization_utils.cc:432</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_aac68304831a1bc81557fb03623a619d6"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#aac68304831a1bc81557fb03623a619d6">operations_research::pdlp::ApplyRescaling</a></div><div class="ttdeci">ScalingVectors ApplyRescaling(const RescalingOptions &rescaling_options, ShardedQuadraticProgram &sharded_qp)</div><div class="ttdef"><b>Definition:</b> <a href="sharded__optimization__utils_8cc_source.html#l00439">sharded_optimization_utils.cc:439</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_ab315578d37cb2f5e1111b0176254cb84"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#ab315578d37cb2f5e1111b0176254cb84">operations_research::pdlp::ComputeStats</a></div><div class="ttdeci">QuadraticProgramStats ComputeStats(const ShardedQuadraticProgram &qp, const double infinite_constraint_bound_threshold)</div><div class="ttdef"><b>Definition:</b> <a href="sharded__optimization__utils_8cc_source.html#l00303">sharded_optimization_utils.cc:303</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_acb7f29f435d6c9fc53148ee403c7049e"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#acb7f29f435d6c9fc53148ee403c7049e">operations_research::pdlp::ProjectToPrimalVariableBounds</a></div><div class="ttdeci">void ProjectToPrimalVariableBounds(const ShardedQuadraticProgram &sharded_qp, VectorXd &primal)</div><div class="ttdef"><b>Definition:</b> <a href="sharded__optimization__utils_8cc_source.html#l00751">sharded_optimization_utils.cc:751</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_ad795f23e2a85f4ae05efa1e2d0e0de4d"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#ad795f23e2a85f4ae05efa1e2d0e0de4d">operations_research::pdlp::TestLp</a></div><div class="ttdeci">QuadraticProgram TestLp()</div><div class="ttdef"><b>Definition:</b> <a href="test__util_8cc_source.html#l00032">test_util.cc:32</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_af2390d8030d8925da948d466b7075d39"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#af2390d8030d8925da948d466b7075d39">operations_research::pdlp::SmallPrimalInfeasibleLp</a></div><div class="ttdeci">QuadraticProgram SmallPrimalInfeasibleLp()</div><div class="ttdef"><b>Definition:</b> <a href="test__util_8cc_source.html#l00216">test_util.cc:216</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_1_1pdlp_html_afaedaf1e3ebe4d6d1a36e3fd1f206de6"><div class="ttname"><a href="namespaceoperations__research_1_1pdlp.html#afaedaf1e3ebe4d6d1a36e3fd1f206de6">operations_research::pdlp::TestDiagonalQp1</a></div><div class="ttdeci">QuadraticProgram TestDiagonalQp1()</div><div class="ttdef"><b>Definition:</b> <a href="test__util_8cc_source.html#l00142">test_util.cc:142</a></div></div>
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<div class="ttc" id="anamespaceoperations__research_html_a817553ad64738460e5c339f24fe5ea13"><div class="ttname"><a href="namespaceoperations__research.html#a817553ad64738460e5c339f24fe5ea13">operations_research::TEST</a></div><div class="ttdeci">TEST(LinearAssignmentTest, NullMatrix)</div><div class="ttdef"><b>Definition:</b> <a href="hungarian__test_8cc_source.html#l00074">hungarian_test.cc:74</a></div></div>
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<div class="ttc" id="aquadratic__program_8h_html"><div class="ttname"><a href="quadratic__program_8h.html">quadratic_program.h</a></div></div>
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<div class="ttc" id="asharded__optimization__utils_8cc_html_a2e50537b138c27e2e30e8d3a568fbee0"><div class="ttname"><a href="sharded__optimization__utils_8cc.html#a2e50537b138c27e2e30e8d3a568fbee0">average</a></div><div class="ttdeci">double average</div><div class="ttdef"><b>Definition:</b> <a href="sharded__optimization__utils_8cc_source.html#l00101">sharded_optimization_utils.cc:101</a></div></div>
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<div class="ttc" id="asharded__optimization__utils_8h_html"><div class="ttname"><a href="sharded__optimization__utils_8h.html">sharded_optimization_utils.h</a></div></div>
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