NLOPTMinimizer.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
 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 * (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
 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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 */
#include <shogun/lib/config.h>
#include <algorithm>
#include <shogun/optimization/NLOPTMinimizer.h>
#include <shogun/optimization/FirstOrderBoundConstraintsCostFunction.h>
using namespace shogun;

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

NLOPTMinimizer::~NLOPTMinimizer()
{
}

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

void NLOPTMinimizer::init()
{
#ifdef HAVE_NLOPT
    m_max_iterations=1000;
    m_variable_tolerance=1e-6;
    m_function_tolerance=1e-6;
    m_nlopt_algorithm=NLOPT_LD_LBFGS;
    m_target_variable=SGVector<float64_t>();
    set_nlopt_parameters();
#endif
}

float64_t NLOPTMinimizer::minimize()
{
#ifdef HAVE_NLOPT
    init_minimization();

    nlopt_opt opt=nlopt_create(m_nlopt_algorithm, m_target_variable.vlen);

    //add bound constraints
    FirstOrderBoundConstraintsCostFunction* bound_constraints_fun
        =dynamic_cast<FirstOrderBoundConstraintsCostFunction *>(m_fun);
    if(bound_constraints_fun)
    {
        SGVector<float64_t> bound=bound_constraints_fun->get_lower_bound();
        if(bound.vlen==1)
        {
            nlopt_set_lower_bounds1(opt, bound[0]);
        }
        else
        {
            REQUIRE(bound.vlen==m_target_variable.vlen,
                "The length of target variable (%d) and the length of lower bound (%d) do not match\n",
                m_target_variable.vlen, bound.vlen);
            nlopt_set_lower_bounds(opt, bound.vector);
        }

        bound=bound_constraints_fun->get_upper_bound();
        if(bound.vlen==1)
        {
            nlopt_set_upper_bounds1(opt, bound[0]);
        }
        else
        {
            REQUIRE(bound.vlen==m_target_variable.vlen,
            "The length of target variable (%d) and the length of upper bound (%d) do not match\n",
                m_target_variable.vlen, bound.vlen);
            nlopt_set_upper_bounds(opt, bound.vector);
        }
    
    }
    // set maximum number of evaluations
    nlopt_set_maxeval(opt, m_max_iterations);
    // set absolute argument tolearance
    nlopt_set_xtol_abs1(opt, m_variable_tolerance);
    nlopt_set_ftol_abs(opt, m_function_tolerance);

    nlopt_set_min_objective(opt, NLOPTMinimizer::nlopt_function, this);

#endif
    // the minimum objective value, upon return
    double cost=0.0;

#ifdef HAVE_NLOPT
    // optimize our function
    nlopt_result error_code=nlopt_optimize(opt, m_target_variable.vector, &cost);
    if(error_code!=1)
    {
        SG_SWARNING("Error(s) happened and NLopt failed during minimization (error code:%d)\n",
            error_code);
    }

    // clean up
    nlopt_destroy(opt);
#endif

    return cost;
}

#ifdef HAVE_NLOPT
void NLOPTMinimizer::set_nlopt_parameters(nlopt_algorithm algorithm,
    float64_t max_iterations,
    float64_t variable_tolerance,
    float64_t function_tolerance)
{
    m_nlopt_algorithm=algorithm;
    m_max_iterations=max_iterations;
    m_variable_tolerance=variable_tolerance;
    m_function_tolerance=function_tolerance;
};

double NLOPTMinimizer::nlopt_function(unsigned dim, const double* variable, double* gradient,
    void* func_data)
{
    NLOPTMinimizer* obj_prt=static_cast<NLOPTMinimizer *>(func_data);
    REQUIRE(obj_prt, "The instance object passed to NLopt 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==(index_t)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);

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

void NLOPTMinimizer::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");
}
#endif