コインまたはCBCソルバーのパフォーマンス：コマンドラインユーティリティとコンパイル済みのC++プログラム

Question

CBCのC++ APIを学習していますが、MPSファイルをロードして解決するコンパイル済みC++プログラムのパフォーマンスに問題がありますそれはCBCコマンドラインユーティリティを開くだけで、同じファイルをインポートしてsolveを使用するのと比べて、CbcModelクラスを使用します。 cmdラインユーティリティは1秒でMIPを解決し、C++プログラムは10分で<で終了しません。

私は、C++ APIを使用しているときにすべてのパラメータを明示的に設定する必要があり、cmdラインユーティリティで使用されるデフォルトのパラメータが普通のMIPモデルではかなり丸いと思われると考えました。

コマンドラインユーティリティで使用されるプリソルブ、ヒューリスティックス、およびカットのデフォルトパラメータのリストがあります。これは、パフォーマンスに合わせてC++プログラムで有効にする必要があります。たぶん、誰かがこれらのパラメータで遊んで経験的に良い一連のパラメータを見つけたでしょう。

C++プログラムは次のとおりです。

int main() 
{ 
    OsiClpSolverInterface solver1; 
    solver1.setLogLevel(0); 

    // Read in example model in MPS file format 
    // and assert that it is a clean model 
    int numMpsReadErrors = solver1.readMps("generic_mip.mps",""); 
    assert(numMpsReadErrors==0); 

    // Pass the solver with the problem to be solved to CbcModel 
    CbcModel model(solver1); 
    model.setLogLevel(0); 

    // Add clique cut generator 
    CglClique clique_generator; 
    model.addCutGenerator(&clique_generator,-1, "Clique"); 

    // Add rounding heuristic 
    CglMixedIntegerRounding mixedGen; 
    model.addCutGenerator(&mixedGen, -1, "Rounding"); 

    model.setNumberThreads(4); 

    model.messageHandler()->setPrefix(false); 

    model.branchAndBound(); 


    const double * solution = model.bestSolution(); 

    printf("Optimal value is %.2f", *solution); 

    return 0; 
} 

問題のMIPモデルはHEREからダウンロードすることができます。最適な目標値：-771.2957。多分this part of the official code helps

Continuous objective value is -798.689 - 0.03 seconds 
Cgl0002I 21 variables fixed 
Cgl0003I 0 fixed, 175 tightened bounds, 1972 strengthened rows, 0 substitutions 
Cgl0004I processed model has 3731 rows, 3835 columns (3835 integer (3660 of which binary)) and 37873 elements 
Cbc0038I Initial state - 365 integers unsatisfied sum - 129.125 
Cbc0038I Pass 1: (0.18 seconds) suminf. 58.66667 (121) obj. -572.133 iterations 510 
Cbc0038I Pass 2: (0.18 seconds) suminf. 58.66667 (121) obj. -572.133 iterations 23 
Cbc0038I Pass 3: (0.18 seconds) suminf. 58.66667 (121) obj. -572.133 iterations 1 
Cbc0038I Pass 4: (0.20 seconds) suminf. 69.00000 (138) obj. -299.496 iterations 589 
Cbc0038I Pass 5: (0.20 seconds) suminf. 54.00000 (109) obj. -287.063 iterations 194 
Cbc0038I Pass 6: (0.21 seconds) suminf. 54.00000 (109) obj. -287.063 iterations 12 
Cbc0038I Pass 7: (0.21 seconds) suminf. 49.00000 (100) obj. -273.321 iterations 33 
Cbc0038I Pass 8: (0.22 seconds) suminf. 48.00000 (97) obj. -269.421 iterations 14 
Cbc0038I Pass 9: (0.22 seconds) suminf. 48.00000 (98) obj. -268.624 iterations 8 
Cbc0038I Pass 10: (0.23 seconds) suminf. 48.00000 (97) obj. -264.813 iterations 4 
Cbc0038I Pass 11: (0.23 seconds) suminf. 47.00000 (94) obj. -261.75 iterations 8 
Cbc0038I Pass 12: (0.24 seconds) suminf. 47.00000 (94) obj. -261.75 iterations 3 
Cbc0038I Pass 13: (0.24 seconds) suminf. 47.00000 (94) obj. -261.75 iterations 3 
Cbc0038I Pass 14: (0.25 seconds) suminf. 57.75000 (118) obj. -103.115 iterations 508 
Cbc0038I Pass 15: (0.26 seconds) suminf. 49.00000 (98) obj. -97.4793 iterations 163 
Cbc0038I Pass 16: (0.26 seconds) suminf. 49.00000 (98) obj. -97.4793 iterations 3 
Cbc0038I Pass 17: (0.27 seconds) suminf. 48.75000 (98) obj. -101.421 iterations 24 
Cbc0038I Pass 18: (0.27 seconds) suminf. 47.00000 (94) obj. -103.346 iterations 25 
Cbc0038I Pass 19: (0.28 seconds) suminf. 47.00000 (94) obj. -103.346 iterations 2 
Cbc0038I Pass 20: (0.28 seconds) suminf. 47.00000 (94) obj. -103.346 iterations 21 
Cbc0038I Pass 21: (0.29 seconds) suminf. 51.50000 (107) obj. 60.0315 iterations 469 
Cbc0038I Pass 22: (0.30 seconds) suminf. 40.00000 (80) obj. 59.913 iterations 168 
Cbc0038I Pass 23: (0.30 seconds) suminf. 40.00000 (80) obj. 59.913 iterations 2 
Cbc0038I Pass 24: (0.31 seconds) suminf. 39.50000 (79) obj. 59.913 iterations 27 
Cbc0038I Pass 25: (0.31 seconds) suminf. 39.00000 (78) obj. 59.913 iterations 23 
Cbc0038I Pass 26: (0.32 seconds) suminf. 39.00000 (78) obj. 59.913 iterations 13 
Cbc0038I Pass 27: (0.33 seconds) suminf. 50.00000 (101) obj. 124.699 iterations 504 
Cbc0038I Pass 28: (0.34 seconds) suminf. 41.00000 (82) obj. 118.624 iterations 174 
Cbc0038I Pass 29: (0.34 seconds) suminf. 41.00000 (82) obj. 118.624 iterations 5 
Cbc0038I Pass 30: (0.34 seconds) suminf. 41.00000 (82) obj. 118.624 iterations 19 
Cbc0038I No solution found this major pass 
Cbc0038I Before mini branch and bound, 2356 integers at bound fixed and 0 continuous 
Cbc0038I Mini branch and bound did not improve solution (0.41 seconds) 
Cbc0038I After 0.41 seconds - Feasibility pump exiting - took 0.25 seconds 
Cbc0031I 583 added rows had average density of 8.2024014 
Cbc0013I At root node, 583 cuts changed objective from -798.68913 to -771.29565 in 10 passes 
Cbc0014I Cut generator 0 (Probing) - 541 row cuts average 2.0 elements, 0 column cuts (0 active) in 0.044 seconds - new frequency is 1 
Cbc0014I Cut generator 1 (Gomory) - 751 row cuts average 116.6 elements, 0 column cuts (0 active) in 0.108 seconds - new frequency is 1 
Cbc0014I Cut generator 2 (Knapsack) - 451 row cuts average 2.0 elements, 0 column cuts (0 active) in 0.040 seconds - new frequency is 1 
Cbc0014I Cut generator 3 (Clique) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.004 seconds - new frequency is -100 
Cbc0014I Cut generator 4 (MixedIntegerRounding2) - 155 row cuts average 16.9 elements, 0 column cuts (0 active) in 0.028 seconds - new frequency is 1 
Cbc0014I Cut generator 5 (FlowCover) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.008 seconds - new frequency is -100 
Cbc0014I Cut generator 6 (TwoMirCuts) - 1171 row cuts average 20.0 elements, 0 column cuts (0 active) in 0.068 seconds - new frequency is 1 
Cbc0010I After 0 nodes, 1 on tree, 1e+50 best solution, best possible -771.29565 (1.18 seconds) 
Cbc0004I Integer solution of -771.29565 found after 2671 iterations and 1 nodes (1.24 seconds) 
Cbc0001I Search completed - best objective -771.2956521739131, took 2671 iterations and 1 nodes (1.24 seconds) 
Cbc0032I Strong branching done 22 times (542 iterations), fathomed 0 nodes and fixed 0 variables 
Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost 
Cuts at root node changed objective from -798.689 to -771.296 
Probing was tried 12 times and created 552 cuts of which 0 were active after adding rounds of cuts (0.044 seconds) 
Gomory was tried 12 times and created 756 cuts of which 0 were active after adding rounds of cuts (0.116 seconds) 
Knapsack was tried 12 times and created 456 cuts of which 0 were active after adding rounds of cuts (0.044 seconds) 
Clique was tried 10 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.004 seconds) 
MixedIntegerRounding2 was tried 12 times and created 155 cuts of which 0 were active after adding rounds of cuts (0.036 seconds) 
FlowCover was tried 10 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.008 seconds) 
TwoMirCuts was tried 12 times and created 1197 cuts of which 0 were active after adding rounds of cuts (0.084 seconds) 
ImplicationCuts was tried 2 times and created 11 cuts of which 0 were active after adding rounds of cuts (0.000 seconds) 

Result - Optimal solution found 

Objective value:    -771.29565217 
Enumerated nodes:    1 
Total iterations:    2671 
Time (CPU seconds):    1.27 
Time (Wallclock seconds):  1.30 

Answer 1

：進歩機能のすべての種類を示す

CBCコマンドラインユーティリティログ（前処理、原始ヒューリスティックと強い分岐）が活性化されます。それはlinedocと呼ばれていますSet up likely cut generators and defaults

CBCのコードは読みにくく、しばらく投資せずにどのようなデフォルト動作があるのか​​分析するのは難しいです。

しかし、上記のリンクされたコードは、一部のcmd-callで有効になったデフォルトのように見えます。

Answer 2

どのコンパイラを使用しますか？ デバッグは有効ですか？最適化が無効ですか？ など。 Visual Studioの場合、これはパフォーマンスに大きな違いをもたらし、コンパイルされたコードがはるかに遅い理由になる可能性があります。