test/mip_test.cc
author Peter Kovacs <kpeter@inf.elte.hu>
Thu, 12 Nov 2009 23:26:13 +0100
changeset 806 fa6f37d7a25b
parent 631 d21b38647e53
child 1113 62dba6c90f35
permissions -rw-r--r--
Entirely rework CapacityScaling (#180)

- Use the new interface similarly to NetworkSimplex.
- Rework the implementation using an efficient internal structure
for handling the residual network. This improvement made the
code much faster (up to 2-5 times faster on large graphs).
- Handle GEQ supply type (LEQ is not supported).
- Handle negative costs for arcs of finite capacity.
(Note that this algorithm cannot handle arcs of negative cost
and infinite upper bound, thus it returns UNBOUNDED if such
an arc exists.)
- Extend the documentation.
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/* -*- mode: C++; indent-tabs-mode: nil; -*-
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 *
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 * This file is a part of LEMON, a generic C++ optimization library.
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 *
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 * Copyright (C) 2003-2009
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 * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport
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 * (Egervary Research Group on Combinatorial Optimization, EGRES).
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 *
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 * Permission to use, modify and distribute this software is granted
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 * provided that this copyright notice appears in all copies. For
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 * precise terms see the accompanying LICENSE file.
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 *
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 * This software is provided "AS IS" with no warranty of any kind,
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 * express or implied, and with no claim as to its suitability for any
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 * purpose.
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 *
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 */
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#include "test_tools.h"
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#include <lemon/config.h>
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#ifdef LEMON_HAVE_CPLEX
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#include <lemon/cplex.h>
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#endif
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#ifdef LEMON_HAVE_GLPK
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#include <lemon/glpk.h>
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#endif
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#ifdef LEMON_HAVE_CBC
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#include <lemon/cbc.h>
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#endif
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using namespace lemon;
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void solveAndCheck(MipSolver& mip, MipSolver::ProblemType stat,
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                   double exp_opt) {
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  using std::string;
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  mip.solve();
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  //int decimal,sign;
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  std::ostringstream buf;
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  buf << "Type should be: " << int(stat)<<" and it is "<<int(mip.type());
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  //  itoa(stat,buf1, 10);
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  check(mip.type()==stat, buf.str());
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  if (stat ==  MipSolver::OPTIMAL) {
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    std::ostringstream sbuf;
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    sbuf << "Wrong optimal value ("<< mip.solValue()
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         <<" instead of " << exp_opt << ")";
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    check(std::abs(mip.solValue()-exp_opt) < 1e-3, sbuf.str());
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    //+ecvt(exp_opt,2)
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  }
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}
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void aTest(MipSolver& mip)
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{
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  //The following example is very simple
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  typedef MipSolver::Row Row;
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  typedef MipSolver::Col Col;
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  Col x1 = mip.addCol();
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  Col x2 = mip.addCol();
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  //Objective function
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  mip.obj(x1);
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  mip.max();
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  //Unconstrained optimization
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  mip.solve();
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  //Check it out!
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  //Constraints
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  mip.addRow(2 * x1 + x2 <= 2);
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  Row y2 = mip.addRow(x1 - 2 * x2 <= 0);
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  //Nonnegativity of the variable x1
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  mip.colLowerBound(x1, 0);
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  //Maximization of x1
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  //over the triangle with vertices (0,0),(4/5,2/5),(0,2)
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  double expected_opt=4.0/5.0;
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  solveAndCheck(mip, MipSolver::OPTIMAL, expected_opt);
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  //Restrict x2 to integer
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  mip.colType(x2,MipSolver::INTEGER);
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  expected_opt=1.0/2.0;
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  solveAndCheck(mip, MipSolver::OPTIMAL, expected_opt);
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  //Restrict both to integer
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  mip.colType(x1,MipSolver::INTEGER);
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  expected_opt=0;
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  solveAndCheck(mip, MipSolver::OPTIMAL, expected_opt);
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  //Erase a variable
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  mip.erase(x2);
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  mip.rowUpperBound(y2, 8);
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  expected_opt=1;
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  solveAndCheck(mip, MipSolver::OPTIMAL, expected_opt);
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}
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template<class MIP>
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void cloneTest()
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{
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  MIP* mip = new MIP();
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  MIP* mipnew = mip->newSolver();
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  MIP* mipclone = mip->cloneSolver();
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  delete mip;
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  delete mipnew;
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  delete mipclone;
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}
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int main()
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{
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#ifdef LEMON_HAVE_GLPK
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  {
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    GlpkMip mip1;
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    aTest(mip1);
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    cloneTest<GlpkMip>();
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  }
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#endif
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#ifdef LEMON_HAVE_CPLEX
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  try {
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    CplexMip mip2;
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    aTest(mip2);
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    cloneTest<CplexMip>();
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  } catch (CplexEnv::LicenseError& error) {
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    check(false, error.what());
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  }
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#endif
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#ifdef LEMON_HAVE_CBC
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  {
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    CbcMip mip1;
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    aTest(mip1);
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    cloneTest<CbcMip>();
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  }
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#endif
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  return 0;
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}