我正在使用 CBC 求解器来求解程序，并且方法 model.branchAndBound 被多次调用（很多次）。因为必须将日志消息写入文件，所以它实际上会减慢程序的速度。我想知道是否可以完全省略日志消息？这是在 c++ 中，我现在认为许多建议的答案纸浆仅适用于 python。此外，如果纸浆是解决这个问题的方法，我不太确定如何使用纸浆。有没有什么好的文档可以帮助理解纸浆和CBC之间的关系，以及如何使用它们？如果有人可以帮助我解决这个问题，我将不胜感激！
例如（这是来自 github 的示例）：
// Copyright (C) 2009, International Business Machines
// Corporation and others. All Rights Reserved.
// This code is licensed under the terms of the Eclipse Public License (EPL).
// For Branch and bound
#include "OsiSolverInterface.hpp"
#include "CbcModel.hpp"
// Methods of building
#include "CoinBuild.hpp"
#include "CoinModel.hpp"
int main(int argc, const char *argv[])
{
OsiClpSolverInterface model;
double objValue[] = { 1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, -1.0 };
// Lower bounds for columns
double columnLower[] = { 2.5, 0.0, 0.0, 0.0, 0.5, 0.0, 0.0, 0.0 };
// Upper bounds for columns
double columnUpper[] = { COIN_DBL_MAX, 4.1, 1.0, 1.0, 4.0,
COIN_DBL_MAX, COIN_DBL_MAX, 4.3 };
// Lower bounds for row activities
double rowLower[] = { 2.5, -COIN_DBL_MAX, 4.0, 1.8, 3.0 };
// Upper bounds for row activities
double rowUpper[] = { COIN_DBL_MAX, 2.1, 4.0, 5.0, 15.0 };
// Matrix stored packed
int column[] = { 0, 1, 3, 4, 7,
1, 2,
2, 5,
3, 6,
0, 4, 7 };
double element[] = { 3.0, 1.0, -2.0, -1.0, -1.0,
2.0, 1.1,
1.0, 1.0,
2.8, -1.2,
5.6, 1.0, 1.9 };
int starts[] = { 0, 5, 7, 9, 11, 14 };
// Integer variables (note upper bound already 1.0)
int whichInt[] = { 2, 3 };
int numberRows = (int)(sizeof(rowLower) / sizeof(double));
int numberColumns = (int)(sizeof(columnLower) / sizeof(double));
CoinModel build;
// First do columns (objective and bounds)
int i;
for (i = 0; i < numberColumns; i++) {
build.setColumnBounds(i, columnLower[i], columnUpper[i]);
build.setObjective(i, objValue[i]);
}
// mark as integer
for (i = 0; i < (int)(sizeof(whichInt) / sizeof(int)); i++)
build.setInteger(whichInt[i]);
// Now build rows
for (i = 0; i < numberRows; i++) {
int startRow = starts[i];
int numberInRow = starts[i + 1] - starts[i];
build.addRow(numberInRow, column + startRow, element + startRow,
rowLower[i], rowUpper[i]);
}
// add rows into solver
model.loadFromCoinModel(build);
CbcModel model2(model);
model2.branchAndBound();
return 0;
}
日志输出是：
Clp0000I Optimal - objective value 3.2368421
Clp0000I Optimal - objective value 3.2368421
Node 0 depth 0 unsatisfied 0 sum 0 obj 3.23684 guess 3.23684 branching on -1
Clp0000I Optimal - objective value 3.2368421
Cbc0004I Integer solution of 3.2368421 found after 0 iterations and 0 nodes (0.13 seconds)
Cbc0001I Search completed - best objective 3.236842105263158, took 0 iterations and 0 nodes (0.14 seconds)
Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost
Clp0000I Optimal - objective value 3.2368421
Clp0000I Optimal - objective value 3.2368421