| // Ceres Solver - A fast non-linear least squares minimizer | 
 | // Copyright 2015 Google Inc. All rights reserved. | 
 | // http://ceres-solver.org/ | 
 | // | 
 | // 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/gradient_problem.h" | 
 | #include "ceres/local_parameterization.h" | 
 | #include "glog/logging.h" | 
 |  | 
 | namespace ceres { | 
 |  | 
 | GradientProblem::GradientProblem(FirstOrderFunction* function) | 
 |     : function_(function), | 
 |       parameterization_( | 
 |           new IdentityParameterization(function_->NumParameters())), | 
 |       scratch_(new double[function_->NumParameters()]) { | 
 | } | 
 |  | 
 | GradientProblem::GradientProblem(FirstOrderFunction* function, | 
 |                                  LocalParameterization* parameterization) | 
 |       : function_(function), | 
 |         parameterization_(parameterization), | 
 |         scratch_(new double[function_->NumParameters()]) { | 
 |   CHECK_EQ(function_->NumParameters(), parameterization_->GlobalSize()); | 
 | } | 
 |  | 
 | int GradientProblem::NumParameters() const { | 
 |   return function_->NumParameters(); | 
 | } | 
 |  | 
 | int GradientProblem::NumLocalParameters() const { | 
 |   return parameterization_->LocalSize(); | 
 | } | 
 |  | 
 |  | 
 | bool GradientProblem::Evaluate(const double* parameters, | 
 |                                double* cost, | 
 |                                double* gradient) const { | 
 |   if (gradient == NULL) { | 
 |     return function_->Evaluate(parameters, cost, NULL); | 
 |   } | 
 |  | 
 |   return (function_->Evaluate(parameters, cost, scratch_.get()) && | 
 |           parameterization_->MultiplyByJacobian(parameters, | 
 |                                                 1, | 
 |                                                 scratch_.get(), | 
 |                                                 gradient)); | 
 | } | 
 |  | 
 | bool GradientProblem::Plus(const double* x, | 
 |                            const double* delta, | 
 |                            double* x_plus_delta) const { | 
 |   return parameterization_->Plus(x, delta, x_plus_delta); | 
 | } | 
 |  | 
 | }  // namespace ceres |