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kpeter (Peter Kovacs)
Improvements and unifications for BellmanFord (#51) - Rework the function type interface to fit to dijkstra(). - Rename named template parameters (Def* -> Set*). - Rename some private member variables (to start with an underscore). - Simplify template parameter names. - Many unifications and improvements in the doc.
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/* -*- mode: C++; indent-tabs-mode: nil; -*-

 *

 * This file is a part of LEMON, a generic C++ optimization library.

 *

 * Copyright (C) 2003-2009

 * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport

 * (Egervary Research Group on Combinatorial Optimization, EGRES).

 *

 * Permission to use, modify and distribute this software is granted

 * provided that this copyright notice appears in all copies. For

 * precise terms see the accompanying LICENSE file.

 *

 * This software is provided "AS IS" with no warranty of any kind,

 * express or implied, and with no claim as to its suitability for any

 * purpose.

 *

 */



#include "test_tools.h"



#include <lemon/config.h>



#ifdef LEMON_HAVE_CPLEX

#include <lemon/cplex.h>

#endif



#ifdef LEMON_HAVE_GLPK

#include <lemon/glpk.h>

#endif



#ifdef LEMON_HAVE_CBC

#include <lemon/cbc.h>

#endif





using namespace lemon;



void solveAndCheck(MipSolver& mip, MipSolver::ProblemType stat,

                   double exp_opt) {

  using std::string;



  mip.solve();

  //int decimal,sign;

  std::ostringstream buf;

  buf << "Type should be: " << int(stat)<<" and it is "<<int(mip.type());





  //  itoa(stat,buf1, 10);

  check(mip.type()==stat, buf.str());



  if (stat ==  MipSolver::OPTIMAL) {

    std::ostringstream sbuf;

    buf << "Wrong optimal value: the right optimum is " << exp_opt;

    check(std::abs(mip.solValue()-exp_opt) < 1e-3, sbuf.str());

    //+ecvt(exp_opt,2)

  }

}



void aTest(MipSolver& mip)

{

  //The following example is very simple





  typedef MipSolver::Row Row;

  typedef MipSolver::Col Col;





  Col x1 = mip.addCol();

  Col x2 = mip.addCol();





  //Objective function

  mip.obj(x1);



  mip.max();



  //Unconstrained optimization

  mip.solve();

  //Check it out!



  //Constraints

  mip.addRow(2 * x1 + x2 <= 2);

  Row y2 = mip.addRow(x1 - 2 * x2 <= 0);



  //Nonnegativity of the variable x1

  mip.colLowerBound(x1, 0);





  //Maximization of x1

  //over the triangle with vertices (0,0),(4/5,2/5),(0,2)

  double expected_opt=4.0/5.0;

  solveAndCheck(mip, MipSolver::OPTIMAL, expected_opt);





  //Restrict x2 to integer

  mip.colType(x2,MipSolver::INTEGER);

  expected_opt=1.0/2.0;

  solveAndCheck(mip, MipSolver::OPTIMAL, expected_opt);





  //Restrict both to integer

  mip.colType(x1,MipSolver::INTEGER);

  expected_opt=0;

  solveAndCheck(mip, MipSolver::OPTIMAL, expected_opt);



  //Erase a variable

  mip.erase(x2);

  mip.rowUpperBound(y2, 8);

  expected_opt=1;

  solveAndCheck(mip, MipSolver::OPTIMAL, expected_opt);



}





template<class MIP>

void cloneTest()

{



  MIP* mip = new MIP();

  MIP* mipnew = mip->newSolver();

  MIP* mipclone = mip->cloneSolver();

  delete mip;

  delete mipnew;

  delete mipclone;

}



int main()

{



#ifdef LEMON_HAVE_GLPK

  {

    GlpkMip mip1;

    aTest(mip1);

    cloneTest<GlpkMip>();

  }

#endif



#ifdef LEMON_HAVE_CPLEX

  try {

    CplexMip mip2;

    aTest(mip2);

    cloneTest<CplexMip>();

  } catch (CplexEnv::LicenseError& error) {

    check(false, error.what());

  }

#endif



#ifdef LEMON_HAVE_CBC

  {

    CbcMip mip1;

    aTest(mip1);

    cloneTest<CbcMip>();

  }

#endif



  return 0;



}