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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2022 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/levenberg_marquardt_strategy.h"
#include <memory>
#include "ceres/internal/eigen.h"
#include "ceres/linear_solver.h"
#include "ceres/trust_region_strategy.h"
#include "glog/logging.h"
#include "gmock/gmock.h"
#include "gmock/mock-log.h"
#include "gtest/gtest.h"
using testing::_;
using testing::AllOf;
using testing::AnyNumber;
using testing::HasSubstr;
using testing::ScopedMockLog;
namespace ceres {
namespace internal {
const double kTolerance = 1e-16;
// Linear solver that takes as input a vector and checks that the
// caller passes the same vector as LinearSolver::PerSolveOptions.D.
class RegularizationCheckingLinearSolver : public DenseSparseMatrixSolver {
public:
RegularizationCheckingLinearSolver(const int num_cols, const double* diagonal)
: num_cols_(num_cols), diagonal_(diagonal) {}
private:
LinearSolver::Summary SolveImpl(
DenseSparseMatrix* A,
const double* b,
const LinearSolver::PerSolveOptions& per_solve_options,
double* x) final {
CHECK(per_solve_options.D != nullptr);
for (int i = 0; i < num_cols_; ++i) {
EXPECT_NEAR(per_solve_options.D[i], diagonal_[i], kTolerance)
<< i << " " << per_solve_options.D[i] << " " << diagonal_[i];
}
return {};
}
const int num_cols_;
const double* diagonal_;
};
TEST(LevenbergMarquardtStrategy, AcceptRejectStepRadiusScaling) {
TrustRegionStrategy::Options options;
options.initial_radius = 2.0;
options.max_radius = 20.0;
options.min_lm_diagonal = 1e-8;
options.max_lm_diagonal = 1e8;
// We need a non-null pointer here, so anything should do.
std::unique_ptr<LinearSolver> linear_solver(
new RegularizationCheckingLinearSolver(0, nullptr));
options.linear_solver = linear_solver.get();
LevenbergMarquardtStrategy lms(options);
EXPECT_EQ(lms.Radius(), options.initial_radius);
lms.StepRejected(0.0);
EXPECT_EQ(lms.Radius(), 1.0);
lms.StepRejected(-1.0);
EXPECT_EQ(lms.Radius(), 0.25);
lms.StepAccepted(1.0);
EXPECT_EQ(lms.Radius(), 0.25 * 3.0);
lms.StepAccepted(1.0);
EXPECT_EQ(lms.Radius(), 0.25 * 3.0 * 3.0);
lms.StepAccepted(0.25);
EXPECT_EQ(lms.Radius(), 0.25 * 3.0 * 3.0 / 1.125);
lms.StepAccepted(1.0);
EXPECT_EQ(lms.Radius(), 0.25 * 3.0 * 3.0 / 1.125 * 3.0);
lms.StepAccepted(1.0);
EXPECT_EQ(lms.Radius(), 0.25 * 3.0 * 3.0 / 1.125 * 3.0 * 3.0);
lms.StepAccepted(1.0);
EXPECT_EQ(lms.Radius(), options.max_radius);
}
TEST(LevenbergMarquardtStrategy, CorrectDiagonalToLinearSolver) {
Matrix jacobian(2, 3);
jacobian.setZero();
jacobian(0, 0) = 0.0;
jacobian(0, 1) = 1.0;
jacobian(1, 1) = 1.0;
jacobian(0, 2) = 100.0;
double residual = 1.0;
double x[3];
DenseSparseMatrix dsm(jacobian);
TrustRegionStrategy::Options options;
options.initial_radius = 2.0;
options.max_radius = 20.0;
options.min_lm_diagonal = 1e-2;
options.max_lm_diagonal = 1e2;
double diagonal[3];
diagonal[0] = options.min_lm_diagonal;
diagonal[1] = 2.0;
diagonal[2] = options.max_lm_diagonal;
for (double& diagonal_entry : diagonal) {
diagonal_entry = sqrt(diagonal_entry / options.initial_radius);
}
RegularizationCheckingLinearSolver linear_solver(3, diagonal);
options.linear_solver = &linear_solver;
LevenbergMarquardtStrategy lms(options);
TrustRegionStrategy::PerSolveOptions pso;
{
ScopedMockLog log;
EXPECT_CALL(log, Log(_, _, _)).Times(AnyNumber());
// This using directive is needed get around the fact that there
// are versions of glog which are not in the google namespace.
using namespace google;
#if defined(GLOG_NO_ABBREVIATED_SEVERITIES)
// Use GLOG_WARNING to support MSVC if GLOG_NO_ABBREVIATED_SEVERITIES
// is defined.
EXPECT_CALL(log,
Log(GLOG_WARNING, _, HasSubstr("Failed to compute a step")));
#else
EXPECT_CALL(log,
Log(google::WARNING, _, HasSubstr("Failed to compute a step")));
#endif
TrustRegionStrategy::Summary summary =
lms.ComputeStep(pso, &dsm, &residual, x);
EXPECT_EQ(summary.termination_type, LINEAR_SOLVER_FAILURE);
}
}
} // namespace internal
} // namespace ceres
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