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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2012 Google Inc. All rights reserved.
// http://code.google.com/p/ceres-solver/
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * 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.
// * Neither the name of Google Inc. nor the names of its contributors may be
// used to endorse or promote products derived from this software without
// specific prior written permission.
//
// 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.
//
// Author: sameeragarwal@google.com (Sameer Agarwal)
#include "ceres/line_search_direction.h"
#include "ceres/line_search_minimizer.h"
#include "ceres/low_rank_inverse_hessian.h"
#include "ceres/internal/eigen.h"
#include "glog/logging.h"
namespace ceres {
namespace internal {
class SteepestDescent : public LineSearchDirection {
public:
virtual ~SteepestDescent() {}
bool NextDirection(const LineSearchMinimizer::State& previous,
const LineSearchMinimizer::State& current,
Vector* search_direction) {
*search_direction = -current.gradient;
return true;
}
};
class NonlinearConjugateGradient : public LineSearchDirection {
public:
NonlinearConjugateGradient(const NonlinearConjugateGradientType type,
const double function_tolerance)
: type_(type),
function_tolerance_(function_tolerance) {
}
bool NextDirection(const LineSearchMinimizer::State& previous,
const LineSearchMinimizer::State& current,
Vector* search_direction) {
double beta = 0.0;
Vector gradient_change;
switch (type_) {
case FLETCHER_REEVES:
beta = current.gradient_squared_norm / previous.gradient_squared_norm;
break;
case POLAK_RIBIRERE:
gradient_change = current.gradient - previous.gradient;
beta = (current.gradient.dot(gradient_change) /
previous.gradient_squared_norm);
break;
case HESTENES_STIEFEL:
gradient_change = current.gradient - previous.gradient;
beta = (current.gradient.dot(gradient_change) /
previous.search_direction.dot(gradient_change));
break;
default:
LOG(FATAL) << "Unknown nonlinear conjugate gradient type: " << type_;
}
*search_direction = -current.gradient + beta * previous.search_direction;
const double directional_derivative =
current. gradient.dot(*search_direction);
if (directional_derivative > -function_tolerance_) {
LOG(WARNING) << "Restarting non-linear conjugate gradients: "
<< directional_derivative;
*search_direction = -current.gradient;
};
return true;
}
private:
const NonlinearConjugateGradientType type_;
const double function_tolerance_;
};
class LBFGS : public LineSearchDirection {
public:
LBFGS(const int num_parameters, const int max_lbfgs_rank)
: low_rank_inverse_hessian_(num_parameters, max_lbfgs_rank) {}
virtual ~LBFGS() {}
bool NextDirection(const LineSearchMinimizer::State& previous,
const LineSearchMinimizer::State& current,
Vector* search_direction) {
low_rank_inverse_hessian_.Update(
previous.search_direction * previous.step_size,
current.gradient - previous.gradient);
search_direction->setZero();
low_rank_inverse_hessian_.RightMultiply(current.gradient.data(),
search_direction->data());
*search_direction *= -1.0;
return true;
}
private:
LowRankInverseHessian low_rank_inverse_hessian_;
};
LineSearchDirection*
LineSearchDirection::Create(const LineSearchDirection::Options& options) {
if (options.type == STEEPEST_DESCENT) {
return new SteepestDescent;
}
if (options.type == NONLINEAR_CONJUGATE_GRADIENT) {
return new NonlinearConjugateGradient(
options.nonlinear_conjugate_gradient_type,
options.function_tolerance);
}
if (options.type == ceres::LBFGS) {
return new ceres::internal::LBFGS(options.num_parameters,
options.max_lbfgs_rank);
}
LOG(ERROR) << "Unknown line search direction type: " << options.type;
return NULL;
}
} // namespace internal
} // namespace ceres
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