RhodeCode

#include "coin/CbcCutGenerator.hpp"

#include "coin/CbcHeuristicLocal.hpp"

#include "coin/CbcHeuristicGreedy.hpp"

#include "coin/CbcHeuristicFPump.hpp"

#include "coin/CbcHeuristicRINS.hpp"

#include "coin/CglGomory.hpp"

#include "coin/CglProbing.hpp"

#include "coin/CglKnapsackCover.hpp"

#include "coin/CglOddHole.hpp"

#include "coin/CglClique.hpp"

#include "coin/CglFlowCover.hpp"

#include "coin/CglMixedIntegerRounding.hpp"

#include "coin/CbcHeuristic.hpp"

namespace lemon {

  CbcMip::CbcMip() {

    _prob = new CoinModel();

    _prob->setProblemName("LEMON");

    _osi_solver = 0;

    _cbc_model = 0;

  }

  CbcMip::CbcMip(const CbcMip& other) {

    _prob = new CoinModel(*other._prob);

    _osi_solver = 0;

    _cbc_model = 0;

  }

  CbcMip::~CbcMip() {

    delete _prob;

    if (_osi_solver) delete _osi_solver;

    if (_cbc_model) delete _cbc_model;

  }

  const char* CbcMip::_solverName() const { return "CbcMip"; }

  int CbcMip::_addCol() {

    _prob->addColumn(0, 0, 0, -COIN_DBL_MAX, COIN_DBL_MAX, 0.0, 0, false);

    return _prob->numberColumns() - 1;

  }

  CbcMip* CbcMip::newSolver() const {

    CbcMip* newlp = new CbcMip;

    return newlp;

  }

  CbcMip* CbcMip::cloneSolver() const {

    CbcMip* copylp = new CbcMip(*this);

    return copylp;

  }

  CbcMip::Value CbcMip::_getObjCoeff(int i) const {

    return _prob->getColumnObjective(i);

  }

  CbcMip::SolveExitStatus CbcMip::_solve() {

    if (_osi_solver) {

      delete _osi_solver;

    }

#ifdef COIN_HAS_CLP

    _osi_solver = new OsiClpSolverInterface();

#elif COIN_HAS_OSL

    _osi_solver = new OsiOslSolverInterface();

#else

#error Cannot instantiate Osi solver

#endif

    _osi_solver->loadFromCoinModel(*_prob);

    if (_cbc_model) {

      delete _cbc_model;

    }

    _cbc_model= new CbcModel(*_osi_solver);

    _cbc_model->initialSolve();

    _cbc_model->solver()->setHintParam(OsiDoReducePrint, true, OsiHintTry);

    if (!_cbc_model->isInitialSolveAbandoned() &&

        _cbc_model->isInitialSolveProvenOptimal() &&

        !_cbc_model->isInitialSolveProvenPrimalInfeasible() &&

        !_cbc_model->isInitialSolveProvenDualInfeasible()) {

      CglProbing generator1;

      generator1.setUsingObjective(true);

      generator1.setMaxPass(3);

      generator1.setMaxProbe(100);

      generator1.setMaxLook(50);

      generator1.setRowCuts(3);

      _cbc_model->addCutGenerator(&generator1, -1, "Probing");

      CglGomory generator2;

      generator2.setLimit(300);

      _cbc_model->addCutGenerator(&generator2, -1, "Gomory");

      CglKnapsackCover generator3;

      _cbc_model->addCutGenerator(&generator3, -1, "Knapsack");

      LEMON_ASSERT(false, "Wrong sense");

    }

  }

  CbcMip::ColTypes CbcMip::_getColType(int i) const {

    return _prob->getColumnIsInteger(i) ? INTEGER : REAL;

  }

  void CbcMip::_clear() {

    delete _prob;

    if (_osi_solver) {

      delete _osi_solver;

      _osi_solver = 0;

    }

    if (_cbc_model) {

      delete _cbc_model;

      _cbc_model = 0;

    }

    _prob = new CoinModel();

    rows.clear();

    cols.clear();

  }

  }

} //END OF NAMESPACE LEMON

    virtual void _setRowLowerBound(int i, Value value);

    virtual Value _getRowLowerBound(int i) const;

    virtual void _setRowUpperBound(int i, Value value);

    virtual Value _getRowUpperBound(int i) const;

    virtual void _setObjCoeffs(ExprIterator b, ExprIterator e);

    virtual void _getObjCoeffs(InsertIterator b) const;

    virtual void _setObjCoeff(int i, Value obj_coef);

    virtual Value _getObjCoeff(int i) const;

    virtual void _setSense(Sense sense);

    virtual Sense _getSense() const;

    virtual ColTypes _getColType(int col) const;

    virtual void _setColType(int col, ColTypes col_type);

    virtual SolveExitStatus _solve();

    virtual ProblemType _getType() const;

    virtual Value _getSol(int i) const;

    virtual Value _getSolValue() const;

    virtual void _clear();

  };

}

#endif

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

 *

 * Copyright (C) 2003-2008

 * 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 <lemon/clp.h>

#include <coin/ClpSimplex.hpp>

namespace lemon {

  ClpLp::ClpLp() {

    _prob = new ClpSimplex();

    _init_temporals();

  }

  ClpLp::ClpLp(const ClpLp& other) {

    _prob = new ClpSimplex(*other._prob);

    rows = other.rows;

    cols = other.cols;

    _init_temporals();

  }

  ClpLp::~ClpLp() {

    delete _prob;

    _clear_temporals();

  }

  void ClpLp::_init_temporals() {

    _primal_ray = 0;

    _dual_ray = 0;

  }

  void ClpLp::_clear_temporals() {

    if (_primal_ray) {

      delete[] _primal_ray;

      _primal_ray = 0;

    }

    if (_dual_ray) {

      delete[] _dual_ray;

      _dual_ray = 0;

    }

  }

  ClpLp* ClpLp::newSolver() const {

    case MAX:

      _prob->setOptimizationDirection(-1);

      break;

    }

  }

  ClpLp::Sense ClpLp::_getSense() const {

    double dir = _prob->optimizationDirection();

    if (dir > 0.0) {

      return MIN;

    } else {

      return MAX;

    }

  }

  void ClpLp::_clear() {

    delete _prob;

    _prob = new ClpSimplex();

    rows.clear();

    cols.clear();

    _col_names_ref.clear();

    _clear_temporals();

  }

  }

} //END OF NAMESPACE LEMON

    virtual void _setObjCoeff(int i, Value obj_coef);

    virtual Value _getObjCoeff(int i) const;

    virtual void _setSense(Sense sense);

    virtual Sense _getSense() const;

    virtual SolveExitStatus _solve();

    virtual Value _getPrimal(int i) const;

    virtual Value _getDual(int i) const;

    virtual Value _getPrimalValue() const;

    virtual Value _getPrimalRay(int i) const;

    virtual Value _getDualRay(int i) const;

    virtual VarStatus _getColStatus(int i) const;

    virtual VarStatus _getRowStatus(int i) const;

    virtual ProblemType _getPrimalType() const;

    virtual ProblemType _getDualType() const;

    virtual void _clear();

  public:

    ///Solves LP with primal simplex method.

    SolveExitStatus solvePrimal();

    ///Solves LP with dual simplex method.

    SolveExitStatus solveDual();

    ///Solves LP with barrier method.

    SolveExitStatus solveBarrier();

    ///Returns the constraint identifier understood by CLP.

    int clpRow(Row r) const { return rows(id(r)); }

    ///Returns the variable identifier understood by CLP.

    int clpCol(Col c) const { return cols(id(c)); }

  };

} //END OF NAMESPACE LEMON

#endif //LEMON_CLP_H

  CplexEnv::CplexEnv(const CplexEnv& other) {

    _env = other._env;

    _cnt = other._cnt;

    ++(*_cnt);

  }

  CplexEnv& CplexEnv::operator=(const CplexEnv& other) {

    _env = other._env;

    _cnt = other._cnt;

    ++(*_cnt);

    return *this;

  }

  CplexEnv::~CplexEnv() {

    --(*_cnt);

    if (*_cnt == 0) {

      delete _cnt;

      CPXcloseCPLEX(&_env);

    }

  }

  CplexBase::CplexBase() : LpBase() {

    int status;

    _prob = CPXcreateprob(cplexEnv(), &status, "Cplex problem");

  }

  CplexBase::CplexBase(const CplexEnv& env)

    : LpBase(), _env(env) {

    int status;

    _prob = CPXcreateprob(cplexEnv(), &status, "Cplex problem");

  }

  CplexBase::CplexBase(const CplexBase& cplex)

    : LpBase() {

    int status;

    _prob = CPXcloneprob(cplexEnv(), cplex._prob, &status);

    rows = cplex.rows;

    cols = cplex.cols;

  }

  CplexBase::~CplexBase() {

    CPXfreeprob(cplexEnv(),&_prob);

  }

  int CplexBase::_addCol() {

    int i = CPXgetnumcols(cplexEnv(), _prob);

    double lb = -INF, ub = INF;

    CPXnewcols(cplexEnv(), _prob, 1, 0, &lb, &ub, 0, 0);

    return i;

  }

  int CplexBase::_addRow() {

    int i = CPXgetnumrows(cplexEnv(), _prob);

    const double ub = INF;

    const char s = 'L';

    CPXnewrows(cplexEnv(), _prob, 1, &ub, &s, 0, 0);

    return i;

  }

  void CplexBase::_eraseCol(int i) {

      break;

    }

  }

  CplexBase::Sense CplexBase::_getSense() const {

    switch (CPXgetobjsen(cplexEnv(), _prob)) {

    case CPX_MIN:

      return MIN;

    case CPX_MAX:

      return MAX;

    default:

      LEMON_ASSERT(false, "Invalid sense");

      return CplexBase::Sense();

    }

  }

  void CplexBase::_clear() {

    CPXfreeprob(cplexEnv(),&_prob);

    int status;

    _prob = CPXcreateprob(cplexEnv(), &status, "Cplex problem");

    rows.clear();

    cols.clear();

  }

  // CplexLp members

  CplexLp::CplexLp()

    : LpBase(), LpSolver(), CplexBase() {}

  CplexLp::CplexLp(const CplexEnv& env)

    : LpBase(), LpSolver(), CplexBase(env) {}

  CplexLp::CplexLp(const CplexLp& other)

    : LpBase(), LpSolver(), CplexBase(other) {}

  CplexLp::~CplexLp() {}

  CplexLp* CplexLp::newSolver() const { return new CplexLp; }

  CplexLp* CplexLp::cloneSolver() const {return new CplexLp(*this); }

  const char* CplexLp::_solverName() const { return "CplexLp"; }

  void CplexLp::_clear_temporals() {

    _col_status.clear();

    _row_status.clear();

    _primal_ray.clear();

    _dual_ray.clear();

  }

    } else {

      return UNSOLVED;

    }

#else

    if (status == 0) {

      //We want to exclude some cases

      switch (CPXgetstat(cplexEnv(), _prob)) {

      case CPX_OBJ_LIM:

      case CPX_IT_LIM_FEAS:

      case CPX_IT_LIM_INFEAS:

      case CPX_TIME_LIM_FEAS:

      case CPX_TIME_LIM_INFEAS:

        return UNSOLVED;

      default:

        return SOLVED;

      }

    } else {

      return UNSOLVED;

    }

#endif

  }

  CplexLp::SolveExitStatus CplexLp::_solve() {

    _clear_temporals();

    return convertStatus(CPXlpopt(cplexEnv(), _prob));

  }

  CplexLp::SolveExitStatus CplexLp::solvePrimal() {

    _clear_temporals();

    return convertStatus(CPXprimopt(cplexEnv(), _prob));

  }

  CplexLp::SolveExitStatus CplexLp::solveDual() {

    _clear_temporals();

    return convertStatus(CPXdualopt(cplexEnv(), _prob));

  }

  CplexLp::SolveExitStatus CplexLp::solveBarrier() {

    _clear_temporals();

    return convertStatus(CPXbaropt(cplexEnv(), _prob));

  }

  CplexLp::Value CplexLp::_getPrimal(int i) const {

    Value x;

    CPXgetx(cplexEnv(), _prob, &x, i, i);

    return x;

  }

  CplexLp::Value CplexLp::_getDual(int i) const {

    Value y;

    CPXgetpi(cplexEnv(), _prob, &y, i, i);

    return y;

  }

  CplexLp::Value CplexLp::_getPrimalValue() const {

    Value objval;

    CPXgetobjval(cplexEnv(), _prob, &objval);

    return objval;

  }

  CplexLp::VarStatus CplexLp::_getColStatus(int i) const {

    if (_col_status.empty()) {

      _col_status.resize(CPXgetnumcols(cplexEnv(), _prob));

      }

    case CPX_AT_UPPER:

      return UPPER;

    default:

      LEMON_ASSERT(false, "Wrong row status");

      return CplexLp::VarStatus();

    }

  }

  CplexLp::Value CplexLp::_getPrimalRay(int i) const {

    if (_primal_ray.empty()) {

      _primal_ray.resize(CPXgetnumcols(cplexEnv(), _prob));

      CPXgetray(cplexEnv(), _prob, &_primal_ray.front());

    }

    return _primal_ray[i];

  }

  CplexLp::Value CplexLp::_getDualRay(int i) const {

    if (_dual_ray.empty()) {

    }

    return _dual_ray[i];

  }

  // This table lists the statuses, returned by the CPXgetstat()

  // routine, for solutions to LP problems or mixed integer problems. If

  // no solution exists, the return value is zero.

  // For Simplex, Barrier

  // 1          CPX_OPTIMAL

  //          Optimal solution found

  // 2          CPX_INFEASIBLE

  //          Problem infeasible

  // 3    CPX_UNBOUNDED

  //          Problem unbounded

  // 4          CPX_OBJ_LIM

  //          Objective limit exceeded in Phase II

  // 5          CPX_IT_LIM_FEAS

  //          Iteration limit exceeded in Phase II

  // 6          CPX_IT_LIM_INFEAS

  //          Iteration limit exceeded in Phase I

  // 7          CPX_TIME_LIM_FEAS

  //          Time limit exceeded in Phase II

  // 8          CPX_TIME_LIM_INFEAS

  //          Time limit exceeded in Phase I

  // 9          CPX_NUM_BEST_FEAS

  //          Problem non-optimal, singularities in Phase II

  // 10         CPX_NUM_BEST_INFEAS

  //          Problem non-optimal, singularities in Phase I

  // 11         CPX_OPTIMAL_INFEAS

  //          Optimal solution found, unscaled infeasibilities

  // 12         CPX_ABORT_FEAS

  //          Aborted in Phase II

  // 13         CPX_ABORT_INFEAS

  //          Aborted in Phase I

  // 14          CPX_ABORT_DUAL_INFEAS

  //          Aborted in barrier, dual infeasible

  // 15          CPX_ABORT_PRIM_INFEAS

  //          Aborted in barrier, primal infeasible

  // 16          CPX_ABORT_PRIM_DUAL_INFEAS

  //          Aborted in barrier, primal and dual infeasible

  // 17          CPX_ABORT_PRIM_DUAL_FEAS

  //          Aborted in barrier, primal and dual feasible

  // 18          CPX_ABORT_CROSSOVER

  //          Aborted in crossover

  // 19          CPX_INForUNBD

  //          Infeasible or unbounded

  // 20   CPX_PIVOT

  //       User pivot used

  //

  // ??case CPX_ABORT_DUAL_INFEAS

  // ??case CPX_ABORT_CROSSOVER

  // ??case CPX_INForUNBD

  // ??case CPX_PIVOT

  //Some more interesting stuff:

  // CPX_PARAM_PROBMETHOD  1062  int  LPMETHOD

  // 0 Automatic

  // 1 Primal Simplex

  // 2 Dual Simplex

  // 3 Network Simplex

  // 4 Standard Barrier

  // Default: 0

  // Description: Method for linear optimization.

  // Determines which algorithm is used when CPXlpopt() (or "optimize"

  // in the Interactive Optimizer) is called. Currently the behavior of

  // the "Automatic" setting is that CPLEX simply invokes the dual

  // simplex method, but this capability may be expanded in the future

  // so that CPLEX chooses the method based on problem characteristics

#if CPX_VERSION < 900

  void statusSwitch(CPXENVptr cplexEnv(),int& stat){

    int lpmethod;

    CPXgetintparam (cplexEnv(),CPX_PARAM_PROBMETHOD,&lpmethod);

    // the unbounded ray can be added to x to give a feasible solution

    // with objective z-1 (or z+1 for maximization models). Thus, if a

    // feasible solution exists, then the optimal objective is

    // unbounded. Note that ILOG CPLEX has not necessarily concluded that

    // a feasible solution exists. Users can call the routine CPXsolninfo

    // to determine whether ILOG CPLEX has also concluded that the model

    // has a feasible solution.

    int stat = CPXgetstat(cplexEnv(), _prob);

#if CPX_VERSION >= 800

    switch (stat)

      {

      case CPX_STAT_OPTIMAL:

        return OPTIMAL;

      case CPX_STAT_UNBOUNDED:

        return UNBOUNDED;

      case CPX_STAT_INFEASIBLE:

        return INFEASIBLE;

      default:

        return UNDEFINED;

      }

#else

    statusSwitch(cplexEnv(),stat);

    //CPXgetstat(cplexEnv(), _prob);

    switch (stat) {

    case 0:

      return UNDEFINED; //Undefined

    case CPX_OPTIMAL://Optimal

      return OPTIMAL;

    case CPX_UNBOUNDED://Unbounded

      return INFEASIBLE;//In case of dual simplex

      //return UNBOUNDED;

    case CPX_INFEASIBLE://Infeasible

      //    case CPX_IT_LIM_INFEAS:

      //     case CPX_TIME_LIM_INFEAS:

      //     case CPX_NUM_BEST_INFEAS:

      //     case CPX_OPTIMAL_INFEAS:

      //     case CPX_ABORT_INFEAS:

      //     case CPX_ABORT_PRIM_INFEAS:

      //     case CPX_ABORT_PRIM_DUAL_INFEAS:

      return UNBOUNDED;//In case of dual simplex

      //return INFEASIBLE;

      //     case CPX_OBJ_LIM:

      //     case CPX_IT_LIM_FEAS:

      //     case CPX_TIME_LIM_FEAS:

      //     case CPX_NUM_BEST_FEAS:

      //     case CPX_ABORT_FEAS:

      //     case CPX_ABORT_PRIM_DUAL_FEAS:

      //       return FEASIBLE;

    default:

      return UNDEFINED; //Everything else comes here

      //FIXME error

    }

#endif

  }

  // CPX_STAT_ABORT_DUAL_OBJ_LIM

  // CPX_STAT_ABORT_IT_LIM

  // CPX_STAT_ABORT_OBJ_LIM

  // CPX_STAT_ABORT_PRIM_OBJ_LIM

  // CPX_STAT_ABORT_TIME_LIM

  // CPX_STAT_ABORT_USER

  // CPX_STAT_FEASIBLE_RELAXED

  // CPX_STAT_INFEASIBLE

  // CPX_STAT_INForUNBD

  // CPX_STAT_NUM_BEST

  // CPX_STAT_OPTIMAL

  // CPX_STAT_OPTIMAL_FACE_UNBOUNDED

  // CPX_STAT_OPTIMAL_INFEAS

  // CPX_STAT_OPTIMAL_RELAXED

  // CPX_STAT_UNBOUNDED

  CplexLp::ProblemType CplexLp::_getDualType() const {

    int stat = CPXgetstat(cplexEnv(), _prob);

#if CPX_VERSION >= 800

    switch (stat) {

    case CPX_STAT_OPTIMAL:

      return OPTIMAL;

    case CPX_STAT_UNBOUNDED:

      return INFEASIBLE;

      CPXchgctype (cplexEnv(), _prob, 1, &i, &t);

    } break;

    default:

      break;

    }

  }

  CplexMip::ColTypes CplexMip::_getColType(int i) const {

    char t;

    CPXgetctype (cplexEnv(), _prob, &t, i, i);

    switch (t) {

    case 'I':

      return INTEGER;

    case 'C':

      return REAL;

    default:

      LEMON_ASSERT(false, "Invalid column type");

      return ColTypes();

    }

  }

  CplexMip::SolveExitStatus CplexMip::_solve() {

    int status;

    status = CPXmipopt (cplexEnv(), _prob);

    if (status==0)

      return SOLVED;

    else

      return UNSOLVED;

  }

  CplexMip::ProblemType CplexMip::_getType() const {

    int stat = CPXgetstat(cplexEnv(), _prob);

    //Fortunately, MIP statuses did not change for cplex 8.0

    switch (stat) {

    case CPXMIP_OPTIMAL:

      // Optimal integer solution has been found.

    case CPXMIP_OPTIMAL_TOL:

      // Optimal soluton with the tolerance defined by epgap or epagap has

      // been found.

      return OPTIMAL;

      //This also exists in later issues

      //    case CPXMIP_UNBOUNDED:

      //return UNBOUNDED;

    virtual void _setColUpperBound(int i, Value value);

    virtual Value _getColUpperBound(int i) const;

  private:

    void _set_row_bounds(int i, Value lb, Value ub);

  protected:

    virtual void _setRowLowerBound(int i, Value value);

    virtual Value _getRowLowerBound(int i) const;

    virtual void _setRowUpperBound(int i, Value value);

    virtual Value _getRowUpperBound(int i) const;

    virtual void _setObjCoeffs(ExprIterator b, ExprIterator e);

    virtual void _getObjCoeffs(InsertIterator b) const;

    virtual void _setObjCoeff(int i, Value obj_coef);

    virtual Value _getObjCoeff(int i) const;

    virtual void _setSense(Sense sense);

    virtual Sense _getSense() const;

    virtual void _clear();

  public:

    /// Returns the used \c CplexEnv instance

    const CplexEnv& env() const { return _env; }

    ///

    const cpxenv* cplexEnv() const { return _env.cplexEnv(); }

    cpxlp* cplexLp() { return _prob; }

    const cpxlp* cplexLp() const { return _prob; }

  };

  /// \brief Interface for the CPLEX LP solver

  ///

  /// This class implements an interface for the CPLEX LP solver.

  ///\ingroup lp_group

  class CplexLp : public LpSolver, public CplexBase {

  public:

    /// \e

    CplexLp();

    /// \e

    CplexLp(const CplexEnv&);

    /// \e

    CplexLp(const CplexLp&);

    /// \e

    virtual ~CplexLp();

    /// \e

    virtual CplexLp* cloneSolver() const;

    /// \e

    virtual CplexLp* newSolver() const;

 * 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.

 *

 */

///\file

///\brief Implementation of the LEMON GLPK LP and MIP solver interface.

#include <lemon/glpk.h>

#include <glpk.h>

#include <lemon/assert.h>

namespace lemon {

  // GlpkBase members

  GlpkBase::GlpkBase() : LpBase() {

    lp = glp_create_prob();

    glp_create_index(lp);

  }

  GlpkBase::GlpkBase(const GlpkBase &other) : LpBase() {

    lp = glp_create_prob();

    glp_copy_prob(lp, other.lp, GLP_ON);

    glp_create_index(lp);

    rows = other.rows;

    cols = other.cols;

  }

  GlpkBase::~GlpkBase() {

    glp_delete_prob(lp);

  }

  int GlpkBase::_addCol() {

    int i = glp_add_cols(lp, 1);

    glp_set_col_bnds(lp, i, GLP_FR, 0.0, 0.0);

    return i;

  }

  int GlpkBase::_addRow() {

    int i = glp_add_rows(lp, 1);

    glp_set_row_bnds(lp, i, GLP_FR, 0.0, 0.0);

    return i;

  }

  void GlpkBase::_eraseCol(int i) {

    int ca[2];

    ca[1] = i;

    glp_del_cols(lp, 1, ca);

  }

  }

  GlpkBase::Sense GlpkBase::_getSense() const {

    switch(glp_get_obj_dir(lp)) {

    case GLP_MIN:

      return MIN;

    case GLP_MAX:

      return MAX;

    default:

      LEMON_ASSERT(false, "Wrong sense");

      return GlpkBase::Sense();

    }

  }

  void GlpkBase::_clear() {

    glp_erase_prob(lp);

    rows.clear();

    cols.clear();

  }

  void GlpkBase::freeEnv() {

    glp_free_env();

  }

  GlpkBase::FreeEnvHelper GlpkBase::freeEnvHelper;

  // GlpkLp members

  GlpkLp::GlpkLp()

    : LpBase(), LpSolver(), GlpkBase() {

    presolver(false);

  }

  GlpkLp::GlpkLp(const GlpkLp& other)

    : LpBase(other), LpSolver(other), GlpkBase(other) {

    presolver(false);

  }

  GlpkLp* GlpkLp::newSolver() const { return new GlpkLp; }

  GlpkLp* GlpkLp::cloneSolver() const { return new GlpkLp(*this); }

  const char* GlpkLp::_solverName() const { return "GlpkLp"; }

  void GlpkLp::_clear_temporals() {

    _primal_ray.clear();

    _dual_ray.clear();

  }

  GlpkLp::SolveExitStatus GlpkLp::_solve() {

    return solvePrimal();

  }

  GlpkLp::SolveExitStatus GlpkLp::solvePrimal() {

    _clear_temporals();

    glp_smcp smcp;

    glp_init_smcp(&smcp);

    smcp.presolve = _presolve;