LBFGSMinimizer.h

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/*
 * Copyright (c) The Shogun Machine Learning Toolbox
 * Written (w) 2015 Wu Lin
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 *    list of conditions and the following disclaimer.
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 *    this list of conditions and the following disclaimer in the documentation
 *    and/or other materials provided with the distribution.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
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 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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 */

#ifndef LBFGSMINIMIZER_H
#define LBFGSMINIMIZER_H
#include <shogun/optimization/FirstOrderMinimizer.h>
#include <shogun/optimization/lbfgs/lbfgs.h>
namespace shogun
{

enum ELBFGSLineSearch
{
    MORETHUENTE=LBFGS_LINESEARCH_MORETHUENTE,
    BACKTRACKING_ARMIJO=LBFGS_LINESEARCH_BACKTRACKING_ARMIJO,
    BACKTRACKING_WOLFE=LBFGS_LINESEARCH_BACKTRACKING_WOLFE,
    BACKTRACKING_STRONG_WOLFE=LBFGS_LINESEARCH_BACKTRACKING_STRONG_WOLFE
};

class LBFGSMinimizer: public FirstOrderMinimizer
{
public:
    /*  Default constructor */
    LBFGSMinimizer();

    LBFGSMinimizer(FirstOrderCostFunction *fun);

    /*  Destructor */
    virtual ~LBFGSMinimizer();

    virtual float64_t minimize();

    virtual bool supports_batch_update() const {return true;}

    virtual void set_lbfgs_parameters(int m = 100,
        int max_linesearch = 1000,
        ELBFGSLineSearch linesearch = BACKTRACKING_STRONG_WOLFE,
        int max_iterations = 1000,
        float64_t delta = 0.0,
        int past = 0,
        float64_t epsilon = 1e-5,
        float64_t min_step = 1e-20,
        float64_t max_step = 1e+20,
        float64_t ftol = 1e-4,
        float64_t wolfe = 0.9,
        float64_t gtol = 0.9,
        float64_t xtol = 1e-16,
        float64_t orthantwise_c = 0.0,
        int orthantwise_start = 0,
        int orthantwise_end = 1);


    virtual CMinimizerContext* save_to_context() {return new CMinimizerContext();}

    virtual void load_from_context(CMinimizerContext* context) {}

private:
    static float64_t evaluate(void *obj,
        const float64_t *variable,
        float64_t *gradient,
        const int dim,
        const float64_t step);

    void init();

protected:
    virtual void init_minimization();

    int m_m;

    int m_max_linesearch;

    int m_linesearch;

    int m_max_iterations;

    float64_t m_delta;

    int m_past;

    float64_t m_epsilon;

    float64_t m_min_step;

    float64_t m_max_step;

    float64_t m_ftol;

    float64_t m_wolfe;

    float64_t m_gtol;

    float64_t m_xtol;

    float64_t m_orthantwise_c;

    int m_orthantwise_start;

    int m_orthantwise_end;

    SGVector<float64_t> m_target_variable;
};

}
#endif