LBFGSMinimizer.cpp

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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;
 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
 * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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#include <shogun/lib/config.h>
#include <algorithm>
#include <shogun/optimization/lbfgs/LBFGSMinimizer.h>

using namespace shogun;

LBFGSMinimizer::LBFGSMinimizer()
    :FirstOrderMinimizer()
{
    init();
}

LBFGSMinimizer::~LBFGSMinimizer()
{
}

LBFGSMinimizer::LBFGSMinimizer(FirstOrderCostFunction *fun)
    :FirstOrderMinimizer(fun)
{
    init();
}

void LBFGSMinimizer::init()
{
    set_lbfgs_parameters();
}

void LBFGSMinimizer::set_lbfgs_parameters(
        int m,
        int max_linesearch,
        ELBFGSLineSearch linesearch,
        int max_iterations,
        float64_t delta,
        int past,
        float64_t epsilon,
        float64_t min_step,
        float64_t max_step,
        float64_t ftol,
        float64_t wolfe,
        float64_t gtol,
        float64_t xtol,
        float64_t orthantwise_c,
        int orthantwise_start,
        int orthantwise_end)
{
    m_m = m;
    m_max_linesearch = max_linesearch;
    m_linesearch = linesearch;
    m_max_iterations = max_iterations;
    m_delta = delta;
    m_past = past;
    m_epsilon = epsilon;
    m_min_step = min_step;
    m_max_step = max_step;
    m_ftol = ftol;
    m_wolfe = wolfe;
    m_gtol = gtol;
    m_xtol = xtol;
    m_orthantwise_c = orthantwise_c;
    m_orthantwise_start = orthantwise_start;
    m_orthantwise_end = orthantwise_end;
}

void LBFGSMinimizer::init_minimization()
{
    REQUIRE(m_fun, "Cost function not set!\n");
    m_target_variable=m_fun->obtain_variable_reference();
    REQUIRE(m_target_variable.vlen>0,"Target variable from cost function must not empty!\n");
}

float64_t LBFGSMinimizer::minimize()
{
    lbfgs_parameter_t lbfgs_param;
    lbfgs_param.m = m_m;
    lbfgs_param.max_linesearch = m_max_linesearch;
    lbfgs_param.linesearch = m_linesearch;
    lbfgs_param.max_iterations = m_max_iterations;
    lbfgs_param.delta = m_delta;
    lbfgs_param.past = m_past;
    lbfgs_param.epsilon = m_epsilon;
    lbfgs_param.min_step = m_min_step;
    lbfgs_param.max_step = m_max_step;
    lbfgs_param.ftol = m_ftol;
    lbfgs_param.wolfe = m_wolfe;
    lbfgs_param.gtol = m_gtol;
    lbfgs_param.xtol = m_xtol;
    lbfgs_param.orthantwise_c = m_orthantwise_c;
    lbfgs_param.orthantwise_start = m_orthantwise_start;
    lbfgs_param.orthantwise_end = m_orthantwise_end;

    init_minimization();

    float64_t cost=0.0;
    int error_code=lbfgs(m_target_variable.vlen, m_target_variable.vector,
        &cost, LBFGSMinimizer::evaluate,
        NULL, this, &lbfgs_param);

    if(error_code!=0 && error_code!=LBFGS_ALREADY_MINIMIZED)
    {
        SG_SWARNING("Error(s) happened during L-BFGS optimization (error code:%d)\n",
            error_code);
    }

    return cost;
}

float64_t LBFGSMinimizer::evaluate(void *obj, const float64_t *variable,
    float64_t *gradient, const int dim, const float64_t step)
{
    /* Note that parameters = parameters_pre_iter - step * gradient_pre_iter */
    LBFGSMinimizer * obj_prt
        = static_cast<LBFGSMinimizer *>(obj);

    REQUIRE(obj_prt, "The instance object passed to L-BFGS optimizer should not be NULL\n");

    //get the gradient wrt variable_new
    SGVector<float64_t> grad=obj_prt->m_fun->get_gradient();
    REQUIRE(grad.vlen==dim,
        "The length of gradient (%d) and the length of variable (%d) do not match\n",
        grad.vlen,dim);

    std::copy(grad.vector,grad.vector+dim,gradient);

    float64_t cost=obj_prt->m_fun->get_cost();
    return cost;
}