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// $Id: longthin.cpp 1898 2013-04-09 18:06:04Z stefan $
// Copyright (C) 2005, International Business Machines
// Corporation and others. All Rights Reserved.
// This code is licensed under the terms of the Eclipse Public License (EPL).
#include <cassert>
#include <iomanip>
#include "CoinPragma.hpp"
// For Branch and bound
#include "OsiSolverInterface.hpp"
#include "CbcModel.hpp"
#include "CbcBranchActual.hpp"
#include "CbcBranchUser.hpp"
#include "CbcCompareUser.hpp"
#include "CbcCutGenerator.hpp"
#include "CbcHeuristicGreedy.hpp"
#include "CbcSolver2.hpp"
#include "CoinModel.hpp"
// Cuts
#include "CglProbing.hpp"
#include "CoinTime.hpp"
/************************************************************************
This main program reads in an integer model from an mps file.
It expects it to be unit coefficients and unit rhs and long and thin
Branching is simple binary branching on integer variables.
*/
int main (int argc, const char *argv[])
{
// Define a Solver for long thin problems
CbcSolver2 solver1;
// Read in model using argv[1]
// and assert that it is a clean model
std::string mpsFileName;
#if defined(SAMPLEDIR)
mpsFileName = SAMPLEDIR "/p0033.mps";
#else
if (argc < 2) {
fprintf(stderr, "Do not know where to find sample MPS files.\n");
exit(1);
}
#endif
if (argc>=2) mpsFileName = argv[1];
int numMpsReadErrors = solver1.readMps(mpsFileName.c_str(),"");
if( numMpsReadErrors != 0 )
{
printf("%d errors reading MPS file\n", numMpsReadErrors);
return numMpsReadErrors;
}
double time1 = CoinCpuTime();
solver1.initialSolve();
// Reduce printout
solver1.setHintParam(OsiDoReducePrint,true,OsiHintTry);
OsiSolverInterface * solver2=&solver1;
CbcModel model(*solver2);
// Point to solver
OsiSolverInterface * solver3 = model.solver();
CbcSolver2 * osiclp = dynamic_cast< CbcSolver2*> (solver3);
assert (osiclp);
osiclp->initialize(&model,NULL);
osiclp->setAlgorithm(2);
osiclp->setMemory(1000);
// Set up some cut generators and defaults
// Probing first as gets tight bounds on continuous
CglProbing generator1;
generator1.setUsingObjective(true);
generator1.setMaxPass(3);
// Number of unsatisfied variables to look at
generator1.setMaxProbe(10);
// How far to follow the consequences
generator1.setMaxLook(50);
// Only look at rows with fewer than this number of elements
generator1.setMaxElements(200);
generator1.setRowCuts(3);
// Add in generators
// Experiment with -1 and -99 etc
model.addCutGenerator(&generator1,-99,"Probing");
// Allow rounding heuristic
CbcRounding heuristic1(model);
model.addHeuristic(&heuristic1);
// And Greedy heuristic
CbcHeuristicGreedyCover heuristic2(model);
// Use original upper and perturb more
heuristic2.setAlgorithm(11);
model.addHeuristic(&heuristic2);
// Redundant definition of default branching (as Default == User)
CbcBranchUserDecision branch;
model.setBranchingMethod(&branch);
// Definition of node choice
CbcCompareUser compare;
model.setNodeComparison(compare);
int iColumn;
int numberColumns = solver3->getNumCols();
// do pseudo costs
CbcObject ** objects = new CbcObject * [numberColumns];
const CoinPackedMatrix * matrix = solver3->getMatrixByCol();
// Column copy
const int * columnLength = matrix->getVectorLengths();
const double * objective = model.getObjCoefficients();
int numberIntegers=0;
for (iColumn=0;iColumn<numberColumns;iColumn++) {
if (solver3->isInteger(iColumn)) {
/* Branching up gets us much closer to an integer solution so we want
to encourage up - so we will branch up if variable value > 0.333333.
The expected cost of going up obviously depends on the cost of the
variable so we just choose pseudo costs to reflect that. We could also
decide to try and use the pseudo costs to make it more likely to branch
on a variable with many coefficients. This leads to the computation below.
*/
double cost = objective[iColumn]*(1.0 + 0.2*((double) columnLength[iColumn]));
CbcSimpleIntegerPseudoCost * newObject =
new CbcSimpleIntegerPseudoCost(&model,iColumn,
2.0*cost,cost);
newObject->setMethod(3);
objects[numberIntegers++]= newObject;
}
}
model.addObjects(numberIntegers,objects);
for (iColumn=0;iColumn<numberIntegers;iColumn++)
delete objects[iColumn];
delete [] objects;
// Do initial solve to continuous
model.initialSolve();
// Do more strong branching if small
// Switch off strong branching if wanted
model.setNumberStrong(5);
// say use resolve for strong branching
osiclp->setSpecialOptions(16);
// We had better allow a lot
model.solver()->setIntParam(OsiMaxNumIterationHotStart,10000);
// So use strategy to keep rows
osiclp->setStrategy(1);
// Switch off most output
if (model.getNumCols()<3000) {
model.messageHandler()->setLogLevel(1);
//model.solver()->messageHandler()->setLogLevel(0);
} else {
model.messageHandler()->setLogLevel(2);
model.solver()->messageHandler()->setLogLevel(1);
}
//model.setPrintFrequency(50);
// Do complete search
try {
model.branchAndBound();
}
catch (CoinError e) {
e.print();
if (e.lineNumber()>=0)
std::cout<<"This was from a CoinAssert"<<std::endl;
exit(0);
}
//void printHowMany();
//printHowMany();
std::cout<<mpsFileName<<" took "<<CoinCpuTime()-time1<<" seconds, "
<<model.getNodeCount()<<" nodes with objective "
<<model.getObjValue()
<<(!model.status() ? " Finished" : " Not finished")
<<std::endl;
// Print solution if finished - we can't get names from Osi!
if (model.getMinimizationObjValue()<1.0e50) {
int numberColumns = model.solver()->getNumCols();
const double * solution = model.solver()->getColSolution();
int iColumn;
std::cout<<std::setiosflags(std::ios::fixed|std::ios::showpoint)<<std::setw(14);
std::cout<<"--------------------------------------"<<std::endl;
for (iColumn=0;iColumn<numberColumns;iColumn++) {
double value=solution[iColumn];
if (fabs(value)>1.0e-7&&model.solver()->isInteger(iColumn))
std::cout<<std::setw(6)<<iColumn<<" "<<value<<std::endl;
}
std::cout<<"--------------------------------------"<<std::endl;
std::cout<<std::resetiosflags(std::ios::fixed|std::ios::showpoint|std::ios::scientific);
}
return 0;
}
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