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// MILP with CBC library, mps
// Finds the solution by using CBC Library
// Code Authors: Akshay Miterani and Pranav Deshpande
#include <sci_iofunc.hpp>
// For Branch and bound
#include "OsiSolverInterface.hpp"
#include "CbcModel.hpp"=
#include "CbcCutGenerator.hpp"
#include "CbcHeuristicLocal.hpp"
#include "OsiClpSolverInterface.hpp"
extern "C" {
#include <api_scilab.h>
int mps_cppintlinprog()
{
OsiClpSolverInterface solver;
// Path to the MPS file
char *mpsFilePath;
// Options to set maximum iterations
double *options;
// Input - 1 or 2 arguments allowed.
CheckInputArgument(pvApiCtx, 2, 2);
// Get the MPS File Path from Scilab
getStringFromScilab(1, &mpsFilePath);
// Receive the options for setting the maximum number of iterations etc.
if( getFixedSizeDoubleMatrixFromScilab(2, 1, 4, &options))
{
return 1;
}
// Read the MPS file
solver.readMps(mpsFilePath);
// Cbc Library used from here
CbcModel model(solver);
model.solver()->setHintParam(OsiDoReducePrint, true, OsiHintTry);
if((int)options[0]!=0)
model.setIntegerTolerance(options[0]);
if((int)options[1]!=0)
model.setMaximumNodes((int)options[1]);
if((int)options[2]!=0)
model.setMaximumSeconds(options[2]);
if((int)options[3]!=0)
model.setAllowableGap(options[3]);
model.branchAndBound();
int nVars = model.getNumCols();
int nCons = model.getNumRows();
const double *val = model.getColSolution();
//Output the solution to Scilab
//get solution for x
double* xValue = model.getColSolution();
//get objective value
double objValue = model.getObjValue();
//Output status
double status_=-1;
if(model.isProvenOptimal()){
status_=0;
}
else if(model.isProvenInfeasible()){
status_=1;
}
else if(model.isSolutionLimitReached()){
status_=2;
}
else if(model. isNodeLimitReached()){
status_=3;
}
else if(model.isAbandoned()){
status_=4;
}
else if(model.isSecondsLimitReached()){
status_=5;
}
else if(model.isContinuousUnbounded()){
status_=6;
}
else if(model.isProvenDualInfeasible()){
status_=7;
}
double nodeCount = model.getNodeCount();
double nfps = model.numberIntegers();
double U = model.getObjValue();
double L = model.getBestPossibleObjValue();
double iterCount = model.getIterationCount();
returnDoubleMatrixToScilab(1 , nVars, 1 , xValue);
returnDoubleMatrixToScilab(2 , 1 , 1 , &objValue);
returnDoubleMatrixToScilab(3 , 1 , 1 , &status_);
returnDoubleMatrixToScilab(4 , 1 , 1 , &nodeCount);
returnDoubleMatrixToScilab(5 , 1 , 1 , &nfps);
returnDoubleMatrixToScilab(6 , 1 , 1 , &L);
returnDoubleMatrixToScilab(7 , 1 , 1 , &U);
returnDoubleMatrixToScilab(8 , 1 , 1 , &iterCount);
return 0;
}
}
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