test/mip_test.cc
author Peter Kovacs <kpeter@inf.elte.hu>
Tue, 24 Mar 2009 00:18:25 +0100
changeset 651 8c3112a66878
parent 484 08d495d48089
child 584 0fec6a017ead
child 587 9db62975c32b
permissions -rw-r--r--
Use XTI implementation instead of ATI in NetworkSimplex (#234)

XTI (eXtended Threaded Index) is an imporved version of the widely
known ATI (Augmented Threaded Index) method for storing and updating
the spanning tree structure in Network Simplex algorithms.

In the ATI data structure three indices are stored for each node:
predecessor, thread and depth. In the XTI data structure depth is
replaced by the number of successors and the last successor
(according to the thread index).
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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) 2003-2008
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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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#ifdef HAVE_CONFIG_H
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#include <lemon/config.h>
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#endif
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#ifdef HAVE_CPLEX
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#include <lemon/cplex.h>
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#endif
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#ifdef HAVE_GLPK
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#include <lemon/glpk.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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    buf << "Wrong optimal value: the right optimum is " << 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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  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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}
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int main()
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{
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#ifdef HAVE_GLPK
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  {
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    GlpkMip mip1;
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    aTest(mip1);
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  }
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#endif
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#ifdef HAVE_CPLEX
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  try {
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    CplexMip mip2;
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    aTest(mip2);
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  } catch (CplexEnv::LicenseError& error) {
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#ifdef LEMON_FORCE_CPLEX_CHECK
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    check(false, error.what());
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#else
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    std::cerr << error.what() << std::endl;
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    std::cerr << "Cplex license check failed, lp check skipped" << std::endl;
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#endif
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  }
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#endif
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  return 0;
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}