我从未使用过那个库,但我认为你应该遵循测试。
核心信息来自自述文件:
如果您希望某些变量是整数,请使用 CelIntVar 而不是 CelNumVar。您还必须将求解器绑定到整数线性规划求解器,例如 Coin-cbc。
查看Rehearse/tests/testRehearse.cpp -> exemple4()(此处显示:不完整的代码;没有复制粘贴):
OsiClpSolverInterface *solver = new OsiClpSolverInterface();
CelModel model(*solver);
...
CelIntVar x1("x1");
...
solver->initialSolve(); // this is the relaxation (and maybe presolving)!
...
CbcModel cbcModel(*solver); // MIP-solver
cbcModel.branchAndBound(); // Use MIP-solver
printf("Solution for x1 : %g\n", model.getSolutionValue(x1, *cbcModel.solver()));
printf("Solution objvalue = : %g\n", cbcModel.solver()->getObjValue());
这种用法(使用 Osi 获得 LP-solver;在 Osi-provided-LP-solver 之上构建 MIP-solver 并调用 brandAndBound)基本上遵循 Cbc 的内部接口(与 python 的cylp这看起来相似)。
作为参考:这是来自此处的官方 CoinOR Cbc(无排练)示例:
// Copyright (C) 2005, International Business Machines
// Corporation and others. All Rights Reserved.
#include "CbcModel.hpp"
// Using CLP as the solver
#include "OsiClpSolverInterface.hpp"
int main (int argc, const char *argv[])
{
OsiClpSolverInterface solver1;
// Read in example model in MPS file format
// and assert that it is a clean model
int numMpsReadErrors = solver1.readMps("../../Mps/Sample/p0033.mps","");
assert(numMpsReadErrors==0);
// Pass the solver with the problem to be solved to CbcModel
CbcModel model(solver1);
// Do complete search
model.branchAndBound();
/* Print the solution. CbcModel clones the solver so we
need to get current copy from the CbcModel */
int numberColumns = model.solver()->getNumCols();
const double * solution = model.bestSolution();
for (int iColumn=0;iColumn<numberColumns;iColumn++) {
double value=solution[iColumn];
if (fabs(value)>1.0e-7&&model.solver()->isInteger(iColumn))
printf("%d has value %g\n",iColumn,value);
}
return 0;
}