| // 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); |
| |
| // Iterations inside the line search algorithm are considered |
| // 'steps' in the broader context, to distinguish these inner |
| // iterations from from the outer iterations of the trust |
| // region minimizer The number of line search steps is the |
| // total number of inner line search iterations (or steps) |
| // across the entire minimization. |
| summary->num_line_search_steps += line_search_summary.num_iterations; |
| 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 |
