<ahref="lp__solver_8h.html">Go to the documentation of this file.</a><divclass="fragment"><divclass="line"><aid="l00001"name="l00001"></a><spanclass="lineno"> 1</span><spanclass="comment">// Copyright 2010-2021 Google LLC</span></div>
<divclass="line"><aid="l00002"name="l00002"></a><spanclass="lineno"> 2</span><spanclass="comment">// Licensed under the Apache License, Version 2.0 (the "License");</span></div>
<divclass="line"><aid="l00003"name="l00003"></a><spanclass="lineno"> 3</span><spanclass="comment">// you may not use this file except in compliance with the License.</span></div>
<divclass="line"><aid="l00004"name="l00004"></a><spanclass="lineno"> 4</span><spanclass="comment">// You may obtain a copy of the License at</span></div>
<divclass="line"><aid="l00008"name="l00008"></a><spanclass="lineno"> 8</span><spanclass="comment">// Unless required by applicable law or agreed to in writing, software</span></div>
<divclass="line"><aid="l00009"name="l00009"></a><spanclass="lineno"> 9</span><spanclass="comment">// distributed under the License is distributed on an "AS IS" BASIS,</span></div>
<divclass="line"><aid="l00010"name="l00010"></a><spanclass="lineno"> 10</span><spanclass="comment">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span></div>
<divclass="line"><aid="l00011"name="l00011"></a><spanclass="lineno"> 11</span><spanclass="comment">// See the License for the specific language governing permissions and</span></div>
<divclass="line"><aid="l00012"name="l00012"></a><spanclass="lineno"> 12</span><spanclass="comment">// limitations under the License.</span></div>
<divclass="line"><aid="l00029"name="l00029"></a><spanclass="lineno"> 29</span><spanclass="comment">// A full-fledged linear programming solver.</span></div>
<divclass="line"><aid="l00034"name="l00034"></a><spanclass="lineno"> 34</span><spanclass="comment">// Sets and gets the solver parameters.</span></div>
<divclass="line"><aid="l00035"name="l00035"></a><spanclass="lineno"> 35</span><spanclass="comment">// See the proto for an extensive documentation.</span></div>
<divclass="line"><aid="l00040"name="l00040"></a><spanclass="lineno"> 40</span><spanclass="comment">// Solves the given linear program and returns the solve status. See the</span></div>
<divclass="line"><aid="l00041"name="l00041"></a><spanclass="lineno"> 41</span><spanclass="comment">// ProblemStatus documentation for a description of the different values.</span></div>
<divclass="line"><aid="l00043"name="l00043"></a><spanclass="lineno"> 43</span><spanclass="comment">// The solution can be retrieved afterwards using the getter functions below.</span></div>
<divclass="line"><aid="l00044"name="l00044"></a><spanclass="lineno"> 44</span><spanclass="comment">// Note that depending on the returned ProblemStatus the solution values may</span></div>
<divclass="line"><aid="l00045"name="l00045"></a><spanclass="lineno"> 45</span><spanclass="comment">// not mean much, so it is important to check the returned status.</span></div>
<divclass="line"><aid="l00047"name="l00047"></a><spanclass="lineno"> 47</span><spanclass="comment">// Incrementality: From one Solve() call to the next, the internal state is</span></div>
<divclass="line"><aid="l00048"name="l00048"></a><spanclass="lineno"> 48</span><spanclass="comment">// not cleared and the solver may take advantage of its current state if the</span></div>
<divclass="line"><aid="l00049"name="l00049"></a><spanclass="lineno"> 49</span><spanclass="comment">// given lp is only slightly modified. If the modification is too important,</span></div>
<divclass="line"><aid="l00050"name="l00050"></a><spanclass="lineno"> 50</span><spanclass="comment">// or if the solver does not see how to reuse the previous state efficiently,</span></div>
<divclass="line"><aid="l00051"name="l00051"></a><spanclass="lineno"> 51</span><spanclass="comment">// it will just solve the problem from scratch. On the other hand, if the lp</span></div>
<divclass="line"><aid="l00052"name="l00052"></a><spanclass="lineno"> 52</span><spanclass="comment">// is the same, calling Solve() again should basically resume the solve from</span></div>
<divclass="line"><aid="l00053"name="l00053"></a><spanclass="lineno"> 53</span><spanclass="comment">// the last position. To disable this behavior, simply call Clear() before.</span></div>
<divclass="line"><aid="l00056"name="l00056"></a><spanclass="lineno"> 56</span><spanclass="comment">// Same as Solve() but use the given time limit rather than constructing a new</span></div>
<divclass="line"><aid="l00057"name="l00057"></a><spanclass="lineno"> 57</span><spanclass="comment">// one from the current GlopParameters.</span></div>
<divclass="line"><aid="l00063"name="l00063"></a><spanclass="lineno"> 63</span><spanclass="comment">// Calling Solve() for the first time, or calling Clear() then Solve() on the</span></div>
<divclass="line"><aid="l00064"name="l00064"></a><spanclass="lineno"> 64</span><spanclass="comment">// same problem is guaranted to be deterministic and to always give the same</span></div>
<divclass="line"><aid="l00065"name="l00065"></a><spanclass="lineno"> 65</span><spanclass="comment">// result, assuming that no time limit was specified.</span></div>
<divclass="line"><aid="l00068"name="l00068"></a><spanclass="lineno"> 68</span><spanclass="comment">// Advanced usage. This should be called before calling Solve(). It will</span></div>
<divclass="line"><aid="l00069"name="l00069"></a><spanclass="lineno"> 69</span><spanclass="comment">// configure the solver to try to start from the given point for the next</span></div>
<divclass="line"><aid="l00070"name="l00070"></a><spanclass="lineno"> 70</span><spanclass="comment">// Solve() only. Note that calling Clear() will invalidate this information.</span></div>
<divclass="line"><aid="l00072"name="l00072"></a><spanclass="lineno"> 72</span><spanclass="comment">// If the set of variables/constraints with a BASIC status does not form a</span></div>
<divclass="line"><aid="l00073"name="l00073"></a><spanclass="lineno"> 73</span><spanclass="comment">// basis a warning will be logged and the code will ignore it. Otherwise, the</span></div>
<divclass="line"><aid="l00074"name="l00074"></a><spanclass="lineno"> 74</span><spanclass="comment">// non-basic variables will be initialized to their given status and solving</span></div>
<divclass="line"><aid="l00075"name="l00075"></a><spanclass="lineno"> 75</span><spanclass="comment">// will start from there (even if the solution is not primal/dual feasible).</span></div>
<divclass="line"><aid="l00077"name="l00077"></a><spanclass="lineno"> 77</span><spanclass="comment">// Important: There is no facility to transform this information in sync with</span></div>
<divclass="line"><aid="l00078"name="l00078"></a><spanclass="lineno"> 78</span><spanclass="comment">// presolve. So you should probably disable presolve when using this since</span></div>
<divclass="line"><aid="l00079"name="l00079"></a><spanclass="lineno"> 79</span><spanclass="comment">// otherwise there is a good chance that the matrix will change and that the</span></div>
<divclass="line"><aid="l00080"name="l00080"></a><spanclass="lineno"> 80</span><spanclass="comment">// given basis will make no sense. Even worse if it happens to be factorizable</span></div>
<divclass="line"><aid="l00081"name="l00081"></a><spanclass="lineno"> 81</span><spanclass="comment">// but doesn't correspond to what was intended.</span></div>
<divclass="line"><aid="l00085"name="l00085"></a><spanclass="lineno"> 85</span><spanclass="comment">// This loads a given solution and computes related quantities so that the</span></div>
<divclass="line"><aid="l00086"name="l00086"></a><spanclass="lineno"> 86</span><spanclass="comment">// getters below will refer to it.</span></div>
<divclass="line"><aid="l00088"name="l00088"></a><spanclass="lineno"> 88</span><spanclass="comment">// Depending on the given solution status, this also checks the solution</span></div>
<divclass="line"><aid="l00089"name="l00089"></a><spanclass="lineno"> 89</span><spanclass="comment">// feasibility or optimality. The exact behavior and tolerances are controlled</span></div>
<divclass="line"><aid="l00090"name="l00090"></a><spanclass="lineno"> 90</span><spanclass="comment">// by the solver parameters. Because of this, the returned ProblemStatus may</span></div>
<divclass="line"><aid="l00091"name="l00091"></a><spanclass="lineno"> 91</span><spanclass="comment">// be changed from the one passed in the ProblemSolution to ABNORMAL or</span></div>
<divclass="line"><aid="l00092"name="l00092"></a><spanclass="lineno"> 92</span><spanclass="comment">// IMPRECISE. Note that this is the same logic as the one used by Solve() to</span></div>
<divclass="line"><aid="l00093"name="l00093"></a><spanclass="lineno"> 93</span><spanclass="comment">// verify the solver solution.</span></div>
<divclass="line"><aid="l00097"name="l00097"></a><spanclass="lineno"> 97</span><spanclass="comment">// Returns the objective value of the solution with its offset and scaling.</span></div>
<divclass="line"><aid="l00100"name="l00100"></a><spanclass="lineno"> 100</span><spanclass="comment">// Accessors to information related to variables.</span></div>
<divclass="line"><aid="l00107"name="l00107"></a><spanclass="lineno"> 107</span><spanclass="comment">// Accessors to information related to constraints. The activity of a</span></div>
<divclass="line"><aid="l00108"name="l00108"></a><spanclass="lineno"> 108</span><spanclass="comment">// constraint is the sum of its linear terms evaluated with variables taking</span></div>
<divclass="line"><aid="l00109"name="l00109"></a><spanclass="lineno"> 109</span><spanclass="comment">// their values at the current solution.</span></div>
<divclass="line"><aid="l00111"name="l00111"></a><spanclass="lineno"> 111</span><spanclass="comment">// Note that the dual_values() do not take into account an eventual objective</span></div>
<divclass="line"><aid="l00112"name="l00112"></a><spanclass="lineno"> 112</span><spanclass="comment">// scaling of the solved LinearProgram.</span></div>
<divclass="line"><aid="l00121"name="l00121"></a><spanclass="lineno"> 121</span><spanclass="comment">// Accessors to information related to unboundedness. A primal ray is returned</span></div>
<divclass="line"><aid="l00122"name="l00122"></a><spanclass="lineno"> 122</span><spanclass="comment">// for primal unbounded problems and a dual ray is returned for dual unbounded</span></div>
<divclass="line"><aid="l00123"name="l00123"></a><spanclass="lineno"> 123</span><spanclass="comment">// problems. constraints_dual_ray corresponds to dual multiplier for</span></div>
<divclass="line"><aid="l00124"name="l00124"></a><spanclass="lineno"> 124</span><spanclass="comment">// constraints and variable_bounds_dual_ray corresponds to dual multipliers</span></div>
<divclass="line"><aid="l00125"name="l00125"></a><spanclass="lineno"> 125</span><spanclass="comment">// for variable bounds (cf. reduced_costs).</span></div>
<divclass="line"><aid="l00134"name="l00134"></a><spanclass="lineno"> 134</span><spanclass="comment">// Returns the primal maximum infeasibility of the solution.</span></div>
<divclass="line"><aid="l00135"name="l00135"></a><spanclass="lineno"> 135</span><spanclass="comment">// This indicates by how much the variable and constraint bounds are violated.</span></div>
<divclass="line"><aid="l00138"name="l00138"></a><spanclass="lineno"> 138</span><spanclass="comment">// Returns the dual maximum infeasibility of the solution.</span></div>
<divclass="line"><aid="l00139"name="l00139"></a><spanclass="lineno"> 139</span><spanclass="comment">// This indicates by how much the variable costs (i.e. objective) should be</span></div>
<divclass="line"><aid="l00140"name="l00140"></a><spanclass="lineno"> 140</span><spanclass="comment">// modified for the solution to be an exact optimal solution.</span></div>
<divclass="line"><aid="l00143"name="l00143"></a><spanclass="lineno"> 143</span><spanclass="comment">// Returns true if the solution status was OPTIMAL and it seems that there is</span></div>
<divclass="line"><aid="l00144"name="l00144"></a><spanclass="lineno"> 144</span><spanclass="comment">// more than one basic optimal solution. Note that this solver always returns</span></div>
<divclass="line"><aid="l00145"name="l00145"></a><spanclass="lineno"> 145</span><spanclass="comment">// an optimal BASIC solution and that there is only a finite number of them.</span></div>
<divclass="line"><aid="l00146"name="l00146"></a><spanclass="lineno"> 146</span><spanclass="comment">// Moreover, given one basic solution, since the basis is always refactorized</span></div>
<divclass="line"><aid="l00147"name="l00147"></a><spanclass="lineno"> 147</span><spanclass="comment">// at optimality before reporting the numerical result, then all the</span></div>
<divclass="line"><aid="l00148"name="l00148"></a><spanclass="lineno"> 148</span><spanclass="comment">// quantities (even the floating point ones) should be always the same.</span></div>
<divclass="line"><aid="l00150"name="l00150"></a><spanclass="lineno"> 150</span><spanclass="comment">// TODO(user): Test this behavior extensively if a client relies on it.</span></div>
<divclass="line"><aid="l00153"name="l00153"></a><spanclass="lineno"> 153</span><spanclass="comment">// Returns the number of simplex iterations used by the last Solve().</span></div>
<divclass="line"><aid="l00156"name="l00156"></a><spanclass="lineno"> 156</span><spanclass="comment">// Returns the "deterministic time" since the creation of the solver. Note</span></div>
<divclass="line"><aid="l00157"name="l00157"></a><spanclass="lineno"> 157</span><spanclass="comment">// That this time is only increased when some operations take place in this</span></div>
<divclass="line"><aid="l00160"name="l00160"></a><spanclass="lineno"> 160</span><spanclass="comment">// TODO(user): Currently, this is only modified when the simplex code is</span></div>
<divclass="line"><aid="l00163"name="l00163"></a><spanclass="lineno"> 163</span><spanclass="comment">// TODO(user): Improve the correlation with the running time.</span></div>
<divclass="line"><aid="l00166"name="l00166"></a><spanclass="lineno"> 166</span><spanclass="comment">// Returns the SolverLogger used during solves.</span></div>
<divclass="line"><aid="l00168"name="l00168"></a><spanclass="lineno"> 168</span><spanclass="comment">// Please note that EnableLogging() and SetLogToStdOut() are reset at the</span></div>
<divclass="line"><aid="l00169"name="l00169"></a><spanclass="lineno"> 169</span><spanclass="comment">// beginning of each solve based on parameters so setting them will have no</span></div>
<divclass="line"><aid="l00174"name="l00174"></a><spanclass="lineno"> 174</span><spanclass="comment">// Resizes all the solution vectors to the given sizes.</span></div>
<divclass="line"><aid="l00175"name="l00175"></a><spanclass="lineno"> 175</span><spanclass="comment">// This is used in case of error to make sure all the getter functions will</span></div>
<divclass="line"><aid="l00176"name="l00176"></a><spanclass="lineno"> 176</span><spanclass="comment">// not crash when given row/col inside the initial linear program dimension.</span></div>
<divclass="line"><aid="l00179"name="l00179"></a><spanclass="lineno"> 179</span><spanclass="comment">// Make sure the primal and dual values are within their bounds in order to</span></div>
<divclass="line"><aid="l00180"name="l00180"></a><spanclass="lineno"> 180</span><spanclass="comment">// have a strong guarantee on the optimal solution. See</span></div>
<divclass="line"><aid="l00181"name="l00181"></a><spanclass="lineno"> 181</span><spanclass="comment">// provide_strong_optimal_guarantee in the GlopParameters proto.</span></div>
<divclass="line"><aid="l00185"name="l00185"></a><spanclass="lineno"> 185</span><spanclass="comment">// Runs the revised simplex algorithm if needed (i.e. if the program was not</span></div>
<divclass="line"><aid="l00186"name="l00186"></a><spanclass="lineno"> 186</span><spanclass="comment">// already solved by the preprocessors).</span></div>
<divclass="line"><aid="l00190"name="l00190"></a><spanclass="lineno"> 190</span><spanclass="comment">// Checks that the returned solution values and statuses are consistent.</span></div>
<divclass="line"><aid="l00191"name="l00191"></a><spanclass="lineno"> 191</span><spanclass="comment">// Returns true if this is the case. See the code for the exact check</span></div>
<divclass="line"><aid="l00196"name="l00196"></a><spanclass="lineno"> 196</span><spanclass="comment">// Returns true if there may be multiple optimal solutions.</span></div>
<divclass="line"><aid="l00197"name="l00197"></a><spanclass="lineno"> 197</span><spanclass="comment">// The return value is true if:</span></div>
<divclass="line"><aid="l00198"name="l00198"></a><spanclass="lineno"> 198</span><spanclass="comment">// - a non-fixed variable, at one of its boumds, has its reduced</span></div>
<divclass="line"><aid="l00199"name="l00199"></a><spanclass="lineno"> 199</span><spanclass="comment">// cost close to zero.</span></div>
<divclass="line"><aid="l00200"name="l00200"></a><spanclass="lineno"> 200</span><spanclass="comment">// or if:</span></div>
<divclass="line"><aid="l00201"name="l00201"></a><spanclass="lineno"> 201</span><spanclass="comment">// - a non-equality constraint (i.e. l <= a.x <= r, with l != r), is at one of</span></div>
<divclass="line"><aid="l00202"name="l00202"></a><spanclass="lineno"> 202</span><spanclass="comment">// its bounds (a.x = r or a.x = l) and has its dual value close to zero.</span></div>
<divclass="line"><aid="l00205"name="l00205"></a><spanclass="lineno"> 205</span><spanclass="comment">// Computes derived quantities from the solution.</span></div>
<divclass="line"><aid="l00209"name="l00209"></a><spanclass="lineno"> 209</span><spanclass="comment">// Computes the primal/dual objectives (without the offset). Note that the</span></div>
<divclass="line"><aid="l00210"name="l00210"></a><spanclass="lineno"> 210</span><spanclass="comment">// dual objective needs the reduced costs in addition to the dual values.</span></div>
<divclass="line"><aid="l00214"name="l00214"></a><spanclass="lineno"> 214</span><spanclass="comment">// Given a relative precision on the primal values of up to</span></div>
<divclass="line"><aid="l00215"name="l00215"></a><spanclass="lineno"> 215</span><spanclass="comment">// solution_feasibility_tolerance(), this returns an upper bound on the</span></div>
<divclass="line"><aid="l00216"name="l00216"></a><spanclass="lineno"> 216</span><spanclass="comment">// expected precision of the objective.</span></div>
<divclass="line"><aid="l00219"name="l00219"></a><spanclass="lineno"> 219</span><spanclass="comment">// Returns the max absolute cost pertubation (resp. rhs perturbation) so that</span></div>
<divclass="line"><aid="l00220"name="l00220"></a><spanclass="lineno"> 220</span><spanclass="comment">// the pair (primal values, dual values) is an EXACT optimal solution to the</span></div>
<divclass="line"><aid="l00221"name="l00221"></a><spanclass="lineno"> 221</span><spanclass="comment">// perturbed problem. Note that this assumes that</span></div>
<divclass="line"><aid="l00222"name="l00222"></a><spanclass="lineno"> 222</span><spanclass="comment">// MovePrimalValuesWithinBounds() and MoveDualValuesWithinBounds() have</span></div>
<divclass="line"><aid="l00223"name="l00223"></a><spanclass="lineno"> 223</span><spanclass="comment">// already been called. The Boolean is_too_large is set to true if any of the</span></div>
<divclass="line"><aid="l00224"name="l00224"></a><spanclass="lineno"> 224</span><spanclass="comment">// perturbation exceed the tolerance (which depends of the coordinate).</span></div>
<divclass="line"><aid="l00226"name="l00226"></a><spanclass="lineno"> 226</span><spanclass="comment">// These bounds are computed using the variable and constraint statuses by</span></div>
<divclass="line"><aid="l00227"name="l00227"></a><spanclass="lineno"> 227</span><spanclass="comment">// enforcing the complementary slackness optimal conditions. Note that they</span></div>
<divclass="line"><aid="l00228"name="l00228"></a><spanclass="lineno"> 228</span><spanclass="comment">// are almost the same as ComputeActivityInfeasibility() and</span></div>
<divclass="line"><aid="l00229"name="l00229"></a><spanclass="lineno"> 229</span><spanclass="comment">// ComputeReducedCostInfeasibility() but looks for optimality rather than just</span></div>
<divclass="line"><aid="l00232"name="l00232"></a><spanclass="lineno"> 232</span><spanclass="comment">// Note(user): We could get EXACT bounds on these perturbations by changing</span></div>
<divclass="line"><aid="l00233"name="l00233"></a><spanclass="lineno"> 233</span><spanclass="comment">// the rounding mode appropriately during these computations. But this is</span></div>
<divclass="line"><aid="l00234"name="l00234"></a><spanclass="lineno"> 234</span><spanclass="comment">// probably not needed.</span></div>
<divclass="line"><aid="l00240"name="l00240"></a><spanclass="lineno"> 240</span><spanclass="comment">// Computes the maximum of the infeasibilities associated with each values.</span></div>
<divclass="line"><aid="l00241"name="l00241"></a><spanclass="lineno"> 241</span><spanclass="comment">// The returned infeasibilities are the maximum of the "absolute" errors of</span></div>
<divclass="line"><aid="l00242"name="l00242"></a><spanclass="lineno"> 242</span><spanclass="comment">// each vector coefficients.</span></div>
<divclass="line"><aid="l00244"name="l00244"></a><spanclass="lineno"> 244</span><spanclass="comment">// These function also set is_too_large to true if any infeasibility is</span></div>
<divclass="line"><aid="l00245"name="l00245"></a><spanclass="lineno"> 245</span><spanclass="comment">// greater than the tolerance (which depends of the coordinate).</span></div>
<divclass="line"><aid="l00255"name="l00255"></a><spanclass="lineno"> 255</span><spanclass="comment">// On a call to Solve(), this is initialized to an exact copy of the given</span></div>
<divclass="line"><aid="l00256"name="l00256"></a><spanclass="lineno"> 256</span><spanclass="comment">// linear program. It is later modified by the preprocessors and then solved</span></div>
<divclass="line"><aid="l00257"name="l00257"></a><spanclass="lineno"> 257</span><spanclass="comment">// by the revised simplex.</span></div>
<divclass="line"><aid="l00259"name="l00259"></a><spanclass="lineno"> 259</span><spanclass="comment">// This is not efficient memory-wise but allows to check optimality with</span></div>
<divclass="line"><aid="l00260"name="l00260"></a><spanclass="lineno"> 260</span><spanclass="comment">// respect to the given LinearProgram that is guaranteed to not have been</span></div>
<divclass="line"><aid="l00261"name="l00261"></a><spanclass="lineno"> 261</span><spanclass="comment">// modified. It also allows for a nicer Solve() API with a const</span></div>
<divclass="line"><aid="l00270"name="l00270"></a><spanclass="lineno"> 270</span><spanclass="comment">// The number of revised simplex iterations used by the last Solve().</span></div>
<divclass="line"><aid="l00273"name="l00273"></a><spanclass="lineno"> 273</span><spanclass="comment">// The current ProblemSolution.</span></div>
<divclass="line"><aid="l00274"name="l00274"></a><spanclass="lineno"> 274</span><spanclass="comment">// TODO(user): use a ProblemSolution directly?</span></div>
<divclass="line"><aid="l00283"name="l00283"></a><spanclass="lineno"> 283</span><spanclass="comment">// Quantities computed from the solution and the linear program.</span></div>
<divclass="line"><aid="l00291"name="l00291"></a><spanclass="lineno"> 291</span><spanclass="comment">// Proto holding all the parameters of the algorithm.</span></div>
<divclass="line"><aid="l00294"name="l00294"></a><spanclass="lineno"> 294</span><spanclass="comment">// The number of times Solve() was called. Used to number dump files.</span></div>
<divclass="ttc"id="aclassoperations__research_1_1_time_limit_html"><divclass="ttname"><ahref="classoperations__research_1_1_time_limit.html">operations_research::TimeLimit</a></div><divclass="ttdoc">A simple class to enforce both an elapsed time limit and a deterministic time limit in the same threa...</div><divclass="ttdef"><b>Definition:</b><ahref="time__limit_8h_source.html#l00106">time_limit.h:106</a></div></div>
<divclass="ttc"id="aclassoperations__research_1_1glop_1_1_l_p_solver_html_a006a984f2cd6bd603cd243639e3491d3"><divclass="ttname"><ahref="classoperations__research_1_1glop_1_1_l_p_solver.html#a006a984f2cd6bd603cd243639e3491d3">operations_research::glop::LPSolver::variable_bounds_dual_ray</a></div><divclass="ttdeci">const DenseRow & variable_bounds_dual_ray() const</div><divclass="ttdef"><b>Definition:</b><ahref="lp__solver_8h_source.html#l00130">lp_solver.h:130</a></div></div>
<divclass="ttc"id="aclassoperations__research_1_1glop_1_1_l_p_solver_html_a01f64f8af66c6c5a66d3b4ebc868cab6"><divclass="ttname"><ahref="classoperations__research_1_1glop_1_1_l_p_solver.html#a01f64f8af66c6c5a66d3b4ebc868cab6">operations_research::glop::LPSolver::GetParameters</a></div><divclass="ttdeci">const GlopParameters & GetParameters() const</div><divclass="ttdef"><b>Definition:</b><ahref="lp__solver_8cc_source.html#l00124">lp_solver.cc:124</a></div></div>
<divclass="ttc"id="aclassoperations__research_1_1glop_1_1_l_p_solver_html_a061b60ddc2f9fced9216694b1366599e"><divclass="ttname"><ahref="classoperations__research_1_1glop_1_1_l_p_solver.html#a061b60ddc2f9fced9216694b1366599e">operations_research::glop::LPSolver::GetSolverLogger</a></div><divclass="ttdeci">SolverLogger & GetSolverLogger()</div><divclass="ttdef"><b>Definition:</b><ahref="lp__solver_8cc_source.html#l00128">lp_solver.cc:128</a></div></div>
<divclass="ttc"id="aclassoperations__research_1_1glop_1_1_l_p_solver_html_a4eed9d69f6b1a5eb13257065c6d3beb1"><divclass="ttname"><ahref="classoperations__research_1_1glop_1_1_l_p_solver.html#a4eed9d69f6b1a5eb13257065c6d3beb1">operations_research::glop::LPSolver::dual_values</a></div><divclass="ttdeci">const DenseColumn & dual_values() const</div><divclass="ttdef"><b>Definition:</b><ahref="lp__solver_8h_source.html#l00113">lp_solver.h:113</a></div></div>
<divclass="ttc"id="aclassoperations__research_1_1glop_1_1_l_p_solver_html_a587e99631d7ffd6bbf51d9fa472e7a57"><divclass="ttname"><ahref="classoperations__research_1_1glop_1_1_l_p_solver.html#a587e99631d7ffd6bbf51d9fa472e7a57">operations_research::glop::LPSolver::variable_statuses</a></div><divclass="ttdeci">const VariableStatusRow & variable_statuses() const</div><divclass="ttdef"><b>Definition:</b><ahref="lp__solver_8h_source.html#l00103">lp_solver.h:103</a></div></div>
<divclass="ttc"id="aclassoperations__research_1_1glop_1_1_l_p_solver_html_a7fab5e220260ac7d973f3f293ea2ad5f"><divclass="ttname"><ahref="classoperations__research_1_1glop_1_1_l_p_solver.html#a7fab5e220260ac7d973f3f293ea2ad5f">operations_research::glop::LPSolver::primal_ray</a></div><divclass="ttdeci">const DenseRow & primal_ray() const</div><divclass="ttdef"><b>Definition:</b><ahref="lp__solver_8h_source.html#l00126">lp_solver.h:126</a></div></div>
<divclass="ttc"id="aclassoperations__research_1_1glop_1_1_l_p_solver_html_a89f0453d28020b22404863dbe8edc61d"><divclass="ttname"><ahref="classoperations__research_1_1glop_1_1_l_p_solver.html#a89f0453d28020b22404863dbe8edc61d">operations_research::glop::LPSolver::constraint_statuses</a></div><divclass="ttdeci">const ConstraintStatusColumn & constraint_statuses() const</div><divclass="ttdef"><b>Definition:</b><ahref="lp__solver_8h_source.html#l00117">lp_solver.h:117</a></div></div>
<divclass="ttc"id="aclassoperations__research_1_1glop_1_1_l_p_solver_html_ab016460ad5e453012eb6a5fe257b8bb3"><divclass="ttname"><ahref="classoperations__research_1_1glop_1_1_l_p_solver.html#ab016460ad5e453012eb6a5fe257b8bb3">operations_research::glop::LPSolver::constraints_dual_ray</a></div><divclass="ttdeci">const DenseColumn & constraints_dual_ray() const</div><divclass="ttdef"><b>Definition:</b><ahref="lp__solver_8h_source.html#l00127">lp_solver.h:127</a></div></div>
<divclass="ttc"id="aclassoperations__research_1_1glop_1_1_l_p_solver_html_ac5d36607cb29127a9ce5f6d6e7c140eb"><divclass="ttname"><ahref="classoperations__research_1_1glop_1_1_l_p_solver.html#ac5d36607cb29127a9ce5f6d6e7c140eb">operations_research::glop::LPSolver::variable_values</a></div><divclass="ttdeci">const DenseRow & variable_values() const</div><divclass="ttdef"><b>Definition:</b><ahref="lp__solver_8h_source.html#l00101">lp_solver.h:101</a></div></div>
<divclass="ttc"id="aclassoperations__research_1_1glop_1_1_l_p_solver_html_ad051b9aae6b4998741c4aae97a458572"><divclass="ttname"><ahref="classoperations__research_1_1glop_1_1_l_p_solver.html#ad051b9aae6b4998741c4aae97a458572">operations_research::glop::LPSolver::reduced_costs</a></div><divclass="ttdeci">const DenseRow & reduced_costs() const</div><divclass="ttdef"><b>Definition:</b><ahref="lp__solver_8h_source.html#l00102">lp_solver.h:102</a></div></div>
<divclass="ttc"id="aclassoperations__research_1_1glop_1_1_l_p_solver_html_aff475d9f1b231153860991e0e981b10e"><divclass="ttname"><ahref="classoperations__research_1_1glop_1_1_l_p_solver.html#aff475d9f1b231153860991e0e981b10e">operations_research::glop::LPSolver::constraint_activities</a></div><divclass="ttdeci">const DenseColumn & constraint_activities() const</div><divclass="ttdef"><b>Definition:</b><ahref="lp__solver_8h_source.html#l00114">lp_solver.h:114</a></div></div>
<divclass="ttc"id="anamespaceoperations__research_html"><divclass="ttname"><ahref="namespaceoperations__research.html">operations_research</a></div><divclass="ttdoc">Collection of objects used to extend the Constraint Solver library.</div><divclass="ttdef"><b>Definition:</b><ahref="dense__doubly__linked__list_8h_source.html#l00021">dense_doubly_linked_list.h:21</a></div></div>
