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// 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 <memory>
#include "ceres/local_parameterization.h"
#include "ceres/manifold_adapter.h"
#include "glog/logging.h"
namespace ceres {
GradientProblem::GradientProblem(FirstOrderFunction* function)
: function_(function),
manifold_(std::make_unique<EuclideanManifold<DYNAMIC>>(
function_->NumParameters())),
scratch_(new double[function_->NumParameters()]) {
CHECK(function != nullptr);
}
GradientProblem::GradientProblem(FirstOrderFunction* function,
LocalParameterization* parameterization)
: function_(function),
parameterization_(parameterization),
scratch_(new double[function_->NumParameters()]) {
CHECK(function != nullptr);
if (parameterization != nullptr) {
manifold_ =
std::make_unique<internal::ManifoldAdapter>(parameterization_.get());
} else {
manifold_ = std::make_unique<EuclideanManifold<DYNAMIC>>(
function_->NumParameters());
}
CHECK_EQ(function_->NumParameters(), manifold_->AmbientSize());
}
GradientProblem::GradientProblem(FirstOrderFunction* function,
Manifold* manifold)
: function_(function), scratch_(new double[function_->NumParameters()]) {
CHECK(function != nullptr);
if (manifold != nullptr) {
manifold_.reset(manifold);
} else {
manifold_ = std::make_unique<EuclideanManifold<DYNAMIC>>(
function_->NumParameters());
}
CHECK_EQ(function_->NumParameters(), manifold_->AmbientSize());
}
int GradientProblem::NumParameters() const {
return function_->NumParameters();
}
int GradientProblem::NumTangentParameters() const {
return manifold_->TangentSize();
}
bool GradientProblem::Evaluate(const double* parameters,
double* cost,
double* gradient) const {
if (gradient == nullptr) {
return function_->Evaluate(parameters, cost, nullptr);
}
return (function_->Evaluate(parameters, cost, scratch_.get()) &&
manifold_->RightMultiplyByPlusJacobian(
parameters, 1, scratch_.get(), gradient));
}
bool GradientProblem::Plus(const double* x,
const double* delta,
double* x_plus_delta) const {
return manifold_->Plus(x, delta, x_plus_delta);
}
} // namespace ceres
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