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MYNT-EYE-S-SDK/3rdparty/ceres-solver-1.11.0/internal/ceres/trust_region_minimizer.cc

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feat(3rdparty): add eigen and ceres
2019-01-03 10:25:18 +02:00
// 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/trust_region_minimizer.h"
#include <algorithm>
#include <cmath>
#include <cstdlib>
#include <cstring>
#include <limits>
#include <string>
#include <vector>
#include "Eigen/Core"
#include "ceres/array_utils.h"
#include "ceres/coordinate_descent_minimizer.h"
#include "ceres/evaluator.h"
#include "ceres/file.h"
#include "ceres/internal/eigen.h"
#include "ceres/internal/scoped_ptr.h"
#include "ceres/line_search.h"
#include "ceres/linear_least_squares_problems.h"
#include "ceres/sparse_matrix.h"
#include "ceres/stringprintf.h"
#include "ceres/trust_region_strategy.h"
#include "ceres/types.h"
#include "ceres/wall_time.h"
#include "glog/logging.h"
namespace ceres {
namespace internal {
namespace {
LineSearch::Summary DoLineSearch(const Minimizer::Options& options,
const Vector& x,
const Vector& gradient,
const double cost,
const Vector& delta,
Evaluator* evaluator) {
LineSearchFunction line_search_function(evaluator);
LineSearch::Options line_search_options;
line_search_options.is_silent = true;
line_search_options.interpolation_type =
options.line_search_interpolation_type;
line_search_options.min_step_size = options.min_line_search_step_size;
line_search_options.sufficient_decrease =
options.line_search_sufficient_function_decrease;
line_search_options.max_step_contraction =
options.max_line_search_step_contraction;
line_search_options.min_step_contraction =
options.min_line_search_step_contraction;
line_search_options.max_num_iterations =
options.max_num_line_search_step_size_iterations;
line_search_options.sufficient_curvature_decrease =
options.line_search_sufficient_curvature_decrease;
line_search_options.max_step_expansion =
options.max_line_search_step_expansion;
line_search_options.function = &line_search_function;
std::string message;
scoped_ptr<LineSearch> line_search(
CHECK_NOTNULL(LineSearch::Create(ceres::ARMIJO,
line_search_options,
&message)));
LineSearch::Summary summary;
line_search_function.Init(x, delta);
line_search->Search(1.0, cost, gradient.dot(delta), &summary);
return summary;
}
} // namespace
// Compute a scaling vector that is used to improve the conditioning
// of the Jacobian.
void TrustRegionMinimizer::EstimateScale(const SparseMatrix& jacobian,
double* scale) const {
jacobian.SquaredColumnNorm(scale);
for (int i = 0; i < jacobian.num_cols(); ++i) {
scale[i] = 1.0 / (1.0 + sqrt(scale[i]));
}
}
void TrustRegionMinimizer::Init(const Minimizer::Options& options) {
options_ = options;
sort(options_.trust_region_minimizer_iterations_to_dump.begin(),
options_.trust_region_minimizer_iterations_to_dump.end());
}
void TrustRegionMinimizer::Minimize(const Minimizer::Options& options,
double* parameters,
Solver::Summary* summary) {
double start_time = WallTimeInSeconds();
double iteration_start_time = start_time;
Init(options);
Evaluator* evaluator = CHECK_NOTNULL(options_.evaluator.get());
SparseMatrix* jacobian = CHECK_NOTNULL(options_.jacobian.get());
TrustRegionStrategy* strategy =
CHECK_NOTNULL(options_.trust_region_strategy.get());
const bool is_not_silent = !options.is_silent;
// If the problem is bounds constrained, then enable the use of a
// line search after the trust region step has been computed. This
// line search will automatically use a projected test point onto
// the feasible set, there by guaranteeing the feasibility of the
// final output.
//
// TODO(sameeragarwal): Make line search available more generally.
const bool use_line_search = options.is_constrained;
summary->termination_type = NO_CONVERGENCE;
summary->num_successful_steps = 0;
summary->num_unsuccessful_steps = 0;
summary->is_constrained = options.is_constrained;
const int num_parameters = evaluator->NumParameters();
const int num_effective_parameters = evaluator->NumEffectiveParameters();
const int num_residuals = evaluator->NumResiduals();
Vector residuals(num_residuals);
Vector trust_region_step(num_effective_parameters);
Vector delta(num_effective_parameters);
Vector x_plus_delta(num_parameters);
Vector gradient(num_effective_parameters);
Vector model_residuals(num_residuals);
Vector scale(num_effective_parameters);
Vector negative_gradient(num_effective_parameters);
Vector projected_gradient_step(num_parameters);
IterationSummary iteration_summary;
iteration_summary.iteration = 0;
iteration_summary.step_is_valid = false;
iteration_summary.step_is_successful = false;
iteration_summary.cost_change = 0.0;
iteration_summary.gradient_max_norm = 0.0;
iteration_summary.gradient_norm = 0.0;
iteration_summary.step_norm = 0.0;
iteration_summary.relative_decrease = 0.0;
iteration_summary.trust_region_radius = strategy->Radius();
iteration_summary.eta = options_.eta;
iteration_summary.linear_solver_iterations = 0;
iteration_summary.step_solver_time_in_seconds = 0;
VectorRef x_min(parameters, num_parameters);
Vector x = x_min;
// Project onto the feasible set.
if (options.is_constrained) {
delta.setZero();
if (!evaluator->Plus(x.data(), delta.data(), x_plus_delta.data())) {
summary->message =
"Unable to project initial point onto the feasible set.";
summary->termination_type = FAILURE;
LOG_IF(WARNING, is_not_silent) << "Terminating: " << summary->message;
return;
}
x_min = x_plus_delta;
x = x_plus_delta;
}
double x_norm = x.norm();
// Do initial cost and Jacobian evaluation.
double cost = 0.0;
if (!evaluator->Evaluate(x.data(),
&cost,
residuals.data(),
gradient.data(),
jacobian)) {
summary->message = "Residual and Jacobian evaluation failed.";
summary->termination_type = FAILURE;
LOG_IF(WARNING, is_not_silent) << "Terminating: " << summary->message;
return;
}
negative_gradient = -gradient;
if (!evaluator->Plus(x.data(),
negative_gradient.data(),
projected_gradient_step.data())) {
summary->message = "Unable to compute gradient step.";
summary->termination_type = FAILURE;
LOG(ERROR) << "Terminating: " << summary->message;
return;
}
summary->initial_cost = cost + summary->fixed_cost;
iteration_summary.cost = cost + summary->fixed_cost;
iteration_summary.gradient_max_norm =
(x - projected_gradient_step).lpNorm<Eigen::Infinity>();
iteration_summary.gradient_norm = (x - projected_gradient_step).norm();
if (iteration_summary.gradient_max_norm <= options.gradient_tolerance) {
summary->message = StringPrintf("Gradient tolerance reached. "
"Gradient max norm: %e <= %e",
iteration_summary.gradient_max_norm,
options_.gradient_tolerance);
summary->termination_type = CONVERGENCE;
VLOG_IF(1, is_not_silent) << "Terminating: " << summary->message;
// Ensure that there is an iteration summary object for iteration
// 0 in Summary::iterations.
iteration_summary.iteration_time_in_seconds =
WallTimeInSeconds() - iteration_start_time;
iteration_summary.cumulative_time_in_seconds =
WallTimeInSeconds() - start_time +
summary->preprocessor_time_in_seconds;
summary->iterations.push_back(iteration_summary);
return;
}
if (options_.jacobi_scaling) {
EstimateScale(*jacobian, scale.data());
jacobian->ScaleColumns(scale.data());
} else {
scale.setOnes();
}
iteration_summary.iteration_time_in_seconds =
WallTimeInSeconds() - iteration_start_time;
iteration_summary.cumulative_time_in_seconds =
WallTimeInSeconds() - start_time
+ summary->preprocessor_time_in_seconds;
summary->iterations.push_back(iteration_summary);
int num_consecutive_nonmonotonic_steps = 0;
double minimum_cost = cost;
double reference_cost = cost;
double accumulated_reference_model_cost_change = 0.0;
double candidate_cost = cost;
double accumulated_candidate_model_cost_change = 0.0;
int num_consecutive_invalid_steps = 0;
bool inner_iterations_are_enabled =
options.inner_iteration_minimizer.get() != NULL;
while (true) {
bool inner_iterations_were_useful = false;
if (!RunCallbacks(options, iteration_summary, summary)) {
return;
}
iteration_start_time = WallTimeInSeconds();
if (iteration_summary.iteration >= options_.max_num_iterations) {
summary->message = "Maximum number of iterations reached.";
summary->termination_type = NO_CONVERGENCE;
VLOG_IF(1, is_not_silent) << "Terminating: " << summary->message;
return;
}
const double total_solver_time = iteration_start_time - start_time +
summary->preprocessor_time_in_seconds;
if (total_solver_time >= options_.max_solver_time_in_seconds) {
summary->message = "Maximum solver time reached.";
summary->termination_type = NO_CONVERGENCE;
VLOG_IF(1, is_not_silent) << "Terminating: " << summary->message;
return;
}
const double strategy_start_time = WallTimeInSeconds();
TrustRegionStrategy::PerSolveOptions per_solve_options;
per_solve_options.eta = options_.eta;
if (find(options_.trust_region_minimizer_iterations_to_dump.begin(),
options_.trust_region_minimizer_iterations_to_dump.end(),
iteration_summary.iteration) !=
options_.trust_region_minimizer_iterations_to_dump.end()) {
per_solve_options.dump_format_type =
options_.trust_region_problem_dump_format_type;
per_solve_options.dump_filename_base =
JoinPath(options_.trust_region_problem_dump_directory,
StringPrintf("ceres_solver_iteration_%03d",
iteration_summary.iteration));
} else {
per_solve_options.dump_format_type = TEXTFILE;
per_solve_options.dump_filename_base.clear();
}
TrustRegionStrategy::Summary strategy_summary =
strategy->ComputeStep(per_solve_options,
jacobian,
residuals.data(),
trust_region_step.data());
if (strategy_summary.termination_type == LINEAR_SOLVER_FATAL_ERROR) {
summary->message =
"Linear solver failed due to unrecoverable "
"non-numeric causes. Please see the error log for clues. ";
summary->termination_type = FAILURE;
LOG_IF(WARNING, is_not_silent) << "Terminating: " << summary->message;
return;
}
iteration_summary = IterationSummary();
iteration_summary.iteration = summary->iterations.back().iteration + 1;
iteration_summary.step_solver_time_in_seconds =
WallTimeInSeconds() - strategy_start_time;
iteration_summary.linear_solver_iterations =
strategy_summary.num_iterations;
iteration_summary.step_is_valid = false;
iteration_summary.step_is_successful = false;
double model_cost_change = 0.0;
if (strategy_summary.termination_type != LINEAR_SOLVER_FAILURE) {
// new_model_cost
// = 1/2 [f + J * step]^2
// = 1/2 [ f'f + 2f'J * step + step' * J' * J * step ]
// model_cost_change
// = cost - new_model_cost
// = f'f/2 - 1/2 [ f'f + 2f'J * step + step' * J' * J * step]
// = -f'J * step - step' * J' * J * step / 2
model_residuals.setZero();
jacobian->RightMultiply(trust_region_step.data(), model_residuals.data());
model_cost_change =
- model_residuals.dot(residuals + model_residuals / 2.0);
if (model_cost_change < 0.0) {
VLOG_IF(1, is_not_silent)
<< "Invalid step: current_cost: " << cost
<< " absolute difference " << model_cost_change
<< " relative difference " << (model_cost_change / cost);
} else {
iteration_summary.step_is_valid = true;
}
}
if (!iteration_summary.step_is_valid) {
// Invalid steps can happen due to a number of reasons, and we
// allow a limited number of successive failures, and return with
// FAILURE if this limit is exceeded.
if (++num_consecutive_invalid_steps >=
options_.max_num_consecutive_invalid_steps) {
summary->message = StringPrintf(
"Number of successive invalid steps more "
"than Solver::Options::max_num_consecutive_invalid_steps: %d",
options_.max_num_consecutive_invalid_steps);
summary->termination_type = FAILURE;
LOG_IF(WARNING, is_not_silent) << "Terminating: " << summary->message;
return;
}
// We are going to try and reduce the trust region radius and
// solve again. To do this, we are going to treat this iteration
// as an unsuccessful iteration. Since the various callbacks are
// still executed, we are going to fill the iteration summary
// with data that assumes a step of length zero and no progress.
iteration_summary.cost = cost + summary->fixed_cost;
iteration_summary.cost_change = 0.0;
iteration_summary.gradient_max_norm =
summary->iterations.back().gradient_max_norm;
iteration_summary.gradient_norm =
summary->iterations.back().gradient_norm;
iteration_summary.step_norm = 0.0;
iteration_summary.relative_decrease = 0.0;
iteration_summary.eta = options_.eta;
} else {
// The step is numerically valid, so now we can judge its quality.
num_consecutive_invalid_steps = 0;
// Undo the Jacobian column scaling.
delta = (trust_region_step.array() * scale.array()).matrix();
// Try improving the step further by using an ARMIJO line
// search.
//
// TODO(sameeragarwal): What happens to trust region sizing as
// it interacts with the line search ?
if (use_line_search) {
const LineSearch::Summary line_search_summary =
DoLineSearch(options, x, gradient, cost, delta, evaluator);
summary->line_search_cost_evaluation_time_in_seconds +=
line_search_summary.cost_evaluation_time_in_seconds;
summary->line_search_gradient_evaluation_time_in_seconds +=
line_search_summary.gradient_evaluation_time_in_seconds;
summary->line_search_polynomial_minimization_time_in_seconds +=
line_search_summary.polynomial_minimization_time_in_seconds;
summary->line_search_total_time_in_seconds +=
line_search_summary.total_time_in_seconds;
if (line_search_summary.success) {
delta *= line_search_summary.optimal_step_size;
}
}
double new_cost = std::numeric_limits<double>::max();
if (evaluator->Plus(x.data(), delta.data(), x_plus_delta.data())) {
if (!evaluator->Evaluate(x_plus_delta.data(),
&new_cost,
NULL,
NULL,
NULL)) {
LOG_IF(WARNING, is_not_silent)
<< "Step failed to evaluate. "
<< "Treating it as a step with infinite cost";
new_cost = std::numeric_limits<double>::max();
}
} else {
LOG_IF(WARNING, is_not_silent)
<< "x_plus_delta = Plus(x, delta) failed. "
<< "Treating it as a step with infinite cost";
}
if (new_cost < std::numeric_limits<double>::max()) {
// Check if performing an inner iteration will make it better.
if (inner_iterations_are_enabled) {
++summary->num_inner_iteration_steps;
double inner_iteration_start_time = WallTimeInSeconds();
const double x_plus_delta_cost = new_cost;
Vector inner_iteration_x = x_plus_delta;
Solver::Summary inner_iteration_summary;
options.inner_iteration_minimizer->Minimize(options,
inner_iteration_x.data(),
&inner_iteration_summary);
if (!evaluator->Evaluate(inner_iteration_x.data(),
&new_cost,
NULL, NULL, NULL)) {
VLOG_IF(2, is_not_silent) << "Inner iteration failed.";
new_cost = x_plus_delta_cost;
} else {
x_plus_delta = inner_iteration_x;
// Boost the model_cost_change, since the inner iteration
// improvements are not accounted for by the trust region.
model_cost_change += x_plus_delta_cost - new_cost;
VLOG_IF(2, is_not_silent)
<< "Inner iteration succeeded; Current cost: " << cost
<< " Trust region step cost: " << x_plus_delta_cost
<< " Inner iteration cost: " << new_cost;
inner_iterations_were_useful = new_cost < cost;
const double inner_iteration_relative_progress =
1.0 - new_cost / x_plus_delta_cost;
// Disable inner iterations once the relative improvement
// drops below tolerance.
inner_iterations_are_enabled =
(inner_iteration_relative_progress >
options.inner_iteration_tolerance);
VLOG_IF(2, is_not_silent && !inner_iterations_are_enabled)
<< "Disabling inner iterations. Progress : "
<< inner_iteration_relative_progress;
}
summary->inner_iteration_time_in_seconds +=
WallTimeInSeconds() - inner_iteration_start_time;
}
}
iteration_summary.step_norm = (x - x_plus_delta).norm();
// Convergence based on parameter_tolerance.
const double step_size_tolerance = options_.parameter_tolerance *
(x_norm + options_.parameter_tolerance);
if (iteration_summary.step_norm <= step_size_tolerance) {
summary->message =
StringPrintf("Parameter tolerance reached. "
"Relative step_norm: %e <= %e.",
(iteration_summary.step_norm /
(x_norm + options_.parameter_tolerance)),
options_.parameter_tolerance);
summary->termination_type = CONVERGENCE;
VLOG_IF(1, is_not_silent) << "Terminating: " << summary->message;
return;
}
iteration_summary.cost_change = cost - new_cost;
const double absolute_function_tolerance =
options_.function_tolerance * cost;
if (fabs(iteration_summary.cost_change) <= absolute_function_tolerance) {
summary->message =
StringPrintf("Function tolerance reached. "
"|cost_change|/cost: %e <= %e",
fabs(iteration_summary.cost_change) / cost,
options_.function_tolerance);
summary->termination_type = CONVERGENCE;
VLOG_IF(1, is_not_silent) << "Terminating: " << summary->message;
return;
}
const double relative_decrease =
iteration_summary.cost_change / model_cost_change;
const double historical_relative_decrease =
(reference_cost - new_cost) /
(accumulated_reference_model_cost_change + model_cost_change);
// If monotonic steps are being used, then the relative_decrease
// is the usual ratio of the change in objective function value
// divided by the change in model cost.
//
// If non-monotonic steps are allowed, then we take the maximum
// of the relative_decrease and the
// historical_relative_decrease, which measures the increase
// from a reference iteration. The model cost change is
// estimated by accumulating the model cost changes since the
// reference iteration. The historical relative_decrease offers
// a boost to a step which is not too bad compared to the
// reference iteration, allowing for non-monotonic steps.
iteration_summary.relative_decrease =
options.use_nonmonotonic_steps
? std::max(relative_decrease, historical_relative_decrease)
: relative_decrease;
// Normally, the quality of a trust region step is measured by
// the ratio
//
// cost_change
// r = -----------------
// model_cost_change
//
// All the change in the nonlinear objective is due to the trust
// region step so this ratio is a good measure of the quality of
// the trust region radius. However, when inner iterations are
// being used, cost_change includes the contribution of the
// inner iterations and its not fair to credit it all to the
// trust region algorithm. So we change the ratio to be
//
// cost_change
// r = ------------------------------------------------
// (model_cost_change + inner_iteration_cost_change)
//
// In most cases this is fine, but it can be the case that the
// change in solution quality due to inner iterations is so large
// and the trust region step is so bad, that this ratio can become
// quite small.
//
// This can cause the trust region loop to reject this step. To
// get around this, we expicitly check if the inner iterations
// led to a net decrease in the objective function value. If
// they did, we accept the step even if the trust region ratio
// is small.
//
// Notice that we do not just check that cost_change is positive
// which is a weaker condition and would render the
// min_relative_decrease threshold useless. Instead, we keep
// track of inner_iterations_were_useful, which is true only
// when inner iterations lead to a net decrease in the cost.
iteration_summary.step_is_successful =
(inner_iterations_were_useful ||
iteration_summary.relative_decrease >
options_.min_relative_decrease);
if (iteration_summary.step_is_successful) {
accumulated_candidate_model_cost_change += model_cost_change;
accumulated_reference_model_cost_change += model_cost_change;
if (!inner_iterations_were_useful &&
relative_decrease <= options_.min_relative_decrease) {
iteration_summary.step_is_nonmonotonic = true;
VLOG_IF(2, is_not_silent)
<< "Non-monotonic step! "
<< " relative_decrease: "
<< relative_decrease
<< " historical_relative_decrease: "
<< historical_relative_decrease;
}
}
}
if (iteration_summary.step_is_successful) {
++summary->num_successful_steps;
strategy->StepAccepted(iteration_summary.relative_decrease);
x = x_plus_delta;
x_norm = x.norm();
// Step looks good, evaluate the residuals and Jacobian at this
// point.
if (!evaluator->Evaluate(x.data(),
&cost,
residuals.data(),
gradient.data(),
jacobian)) {
summary->message = "Residual and Jacobian evaluation failed.";
summary->termination_type = FAILURE;
LOG_IF(WARNING, is_not_silent) << "Terminating: " << summary->message;
return;
}
negative_gradient = -gradient;
if (!evaluator->Plus(x.data(),
negative_gradient.data(),
projected_gradient_step.data())) {
summary->message =
"projected_gradient_step = Plus(x, -gradient) failed.";
summary->termination_type = FAILURE;
LOG(ERROR) << "Terminating: " << summary->message;
return;
}
iteration_summary.gradient_max_norm =
(x - projected_gradient_step).lpNorm<Eigen::Infinity>();
iteration_summary.gradient_norm = (x - projected_gradient_step).norm();
if (options_.jacobi_scaling) {
jacobian->ScaleColumns(scale.data());
}
// Update the best, reference and candidate iterates.
//
// Based on algorithm 10.1.2 (page 357) of "Trust Region
// Methods" by Conn Gould & Toint, or equations 33-40 of
// "Non-monotone trust-region algorithms for nonlinear
// optimization subject to convex constraints" by Phil Toint,
// Mathematical Programming, 77, 1997.
if (cost < minimum_cost) {
// A step that improves solution quality was found.
x_min = x;
minimum_cost = cost;
// Set the candidate iterate to the current point.
candidate_cost = cost;
num_consecutive_nonmonotonic_steps = 0;
accumulated_candidate_model_cost_change = 0.0;
} else {
++num_consecutive_nonmonotonic_steps;
if (cost > candidate_cost) {
// The current iterate is has a higher cost than the
// candidate iterate. Set the candidate to this point.
VLOG_IF(2, is_not_silent)
<< "Updating the candidate iterate to the current point.";
candidate_cost = cost;
accumulated_candidate_model_cost_change = 0.0;
}
// At this point we have made too many non-monotonic steps and
// we are going to reset the value of the reference iterate so
// as to force the algorithm to descend.
//
// This is the case because the candidate iterate has a value
// greater than minimum_cost but smaller than the reference
// iterate.
if (num_consecutive_nonmonotonic_steps ==
options.max_consecutive_nonmonotonic_steps) {
VLOG_IF(2, is_not_silent)
<< "Resetting the reference point to the candidate point";
reference_cost = candidate_cost;
accumulated_reference_model_cost_change =
accumulated_candidate_model_cost_change;
}
}
} else {
++summary->num_unsuccessful_steps;
if (iteration_summary.step_is_valid) {
strategy->StepRejected(iteration_summary.relative_decrease);
} else {
strategy->StepIsInvalid();
}
}
iteration_summary.cost = cost + summary->fixed_cost;
iteration_summary.trust_region_radius = strategy->Radius();
iteration_summary.iteration_time_in_seconds =
WallTimeInSeconds() - iteration_start_time;
iteration_summary.cumulative_time_in_seconds =
WallTimeInSeconds() - start_time
+ summary->preprocessor_time_in_seconds;
summary->iterations.push_back(iteration_summary);
// If the step was successful, check for the gradient norm
// collapsing to zero, and if the step is unsuccessful then check
// if the trust region radius has collapsed to zero.
//
// For correctness (Number of IterationSummary objects, correct
// final cost, and state update) these convergence tests need to
// be performed at the end of the iteration.
if (iteration_summary.step_is_successful) {
// Gradient norm can only go down in successful steps.
if (iteration_summary.gradient_max_norm <= options.gradient_tolerance) {
summary->message = StringPrintf("Gradient tolerance reached. "
"Gradient max norm: %e <= %e",
iteration_summary.gradient_max_norm,
options_.gradient_tolerance);
summary->termination_type = CONVERGENCE;
VLOG_IF(1, is_not_silent) << "Terminating: " << summary->message;
return;
}
} else {
// Trust region radius can only go down if the step if
// unsuccessful.
if (iteration_summary.trust_region_radius <
options_.min_trust_region_radius) {
summary->message = "Termination. Minimum trust region radius reached.";
summary->termination_type = CONVERGENCE;
VLOG_IF(1, is_not_silent) << summary->message;
return;
}
}
}
}
} // namespace internal
} // namespace ceres
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