Cplex.h

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/*
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 3 of the License, or
 * (at your option) any later version.
 *
 * Written (W) 2006-2009 Soeren Sonnenburg
 * Copyright (C) 2006-2009 Fraunhofer Institute FIRST and Max-Planck-Society
 */

#ifndef CCPLEX_H__
#define CCPLEX_H__

#include "lib/config.h"

#ifdef USE_CPLEX
extern "C" {
#include <ilcplex/cplex.h>
}

#include "lib/common.h"
#include "base/SGObject.h"

#include "features/SparseFeatures.h"
#include "features/Labels.h"

namespace shogun
{
enum E_PROB_TYPE
{
    E_LINEAR,
    E_QP
};

#define IGNORE_IN_CLASSLIST
IGNORE_IN_CLASSLIST class CCplex : public CSGObject
{
public:

    CCplex();
    virtual ~CCplex();

    bool init(E_PROB_TYPE t, int32_t timeout=60);
    bool cleanup();

    // A = [ E Z_w Z_x ] dim(A)=(num_dim+1, num_dim+1 + num_zero + num_bound)
    // (+1 for bias!)
    bool setup_subgradientlpm_QP(
        float64_t C, CLabels* labels, CSparseFeatures<float64_t>* features,
        int32_t* idx_bound, int32_t num_bound, int32_t* w_zero,
        int32_t num_zero, float64_t* vee, int32_t num_dim, bool use_bias);

    bool setup_lpboost(float64_t C, int32_t num_cols);
    bool add_lpboost_constraint(
        float64_t factor, TSparseEntry<float64_t>* h, int32_t len,
        int32_t ulen, CLabels* label);

    // given N sparse inputs x_i, and corresponding labels y_i i=0...N-1
    // create the following 1-norm SVM problem & transfer to cplex
    // 
    // min_w        sum_{i=0}^N ( w^+_i + w^-_i) + C \sum_{i=0}^N \xi_i
    // w=[w^+ w^-]
    // b, xi
    // 
    // -y_i((w^+-w^-)^T x_i + b)-xi_i <= -1
    // xi_i >= 0 
    // w_i >= 0    forall i=1...N
    // min f^x
    // Ax <= b
    // -x <= 0
    // 
    // lb= [ -inf, //b
    //    2*dims [0], //w
    //    num_train [0] //xi 
    //  ]
    // 
    // ub= [ inf, //b
    //    2*dims [inf], //w
    //    num_train [inf] //xi 
    //  ]
    // 
    // f= [0,2*dim[1], num_train*C]
    // A= [-y', // b
    //  -y_ix_i // w_+
    //  +y_ix_i // w_-
    //  -1 //xi
    //  ]
    // 
    //  dim(A)=(n,1+2*dim+n)
    // 
    // b =  -1 -1 -1 -1 ... 
    bool setup_lpm(
        float64_t C, CSparseFeatures<float64_t>* x, CLabels* y, bool use_bias);

    // call this to setup linear part
    //
    // setup lp, to minimize
    // objective[0]*x_0 ... objective[cols-1]*x_{cols-1}
    // w.r.t. x
    // s.t. constraint_mat*x <= rhs
    // lb[i] <= x[i] <= ub[i] for all i
    bool setup_lp(
        float64_t* objective, float64_t* constraints_mat, int32_t rows,
        int32_t cols, float64_t* rhs, float64_t* lb, float64_t* ub);


    // call this to setup quadratic part H
    // x'*H*x
    // call setup_lp before (to setup the linear part / linear constraints)
    bool setup_qp(float64_t* H, int32_t dim);
    bool optimize(float64_t* sol, float64_t* lambda=NULL);

    bool dense_to_cplex_sparse(
        float64_t* H, int32_t rows, int32_t cols, int* &qmatbeg, int* &qmatcnt,
        int* &qmatind, double* &qmatval);

    inline bool set_time_limit(float64_t seconds)
    {
        return CPXsetdblparam (env, CPX_PARAM_TILIM, seconds) == 0;
    }
    inline bool write_problem(char* filename)
    {
        return CPXwriteprob (env, lp, filename, NULL) == 0;
    }

    inline bool write_Q(char* filename)
    {
#if CPX_VERSION >= 1000 //CPXqpwrite has been deprecated in CPLEX 10
        return CPXwriteprob (env, lp, filename, NULL) == 0;
#else
        return CPXqpwrite (env, lp, filename) == 0;
#endif
    }

    inline virtual const char* get_name() const { return "Cplex"; }

protected:
  CPXENVptr     env;
  CPXLPptr      lp;
  bool          lp_initialized;

  E_PROB_TYPE problem_type;
};
}
#endif
#endif