| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2014 Google Inc. All rights reserved. |
| // http://code.google.com/p/ceres-solver/ |
| // |
| // 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: keir@google.com (Keir Mierle) |
| |
| #include "ceres/solver_impl.h" |
| |
| #include <cstdio> |
| #include <iostream> // NOLINT |
| #include <numeric> |
| #include <string> |
| #include "ceres/array_utils.h" |
| #include "ceres/callbacks.h" |
| #include "ceres/coordinate_descent_minimizer.h" |
| #include "ceres/cxsparse.h" |
| #include "ceres/evaluator.h" |
| #include "ceres/gradient_checking_cost_function.h" |
| #include "ceres/iteration_callback.h" |
| #include "ceres/levenberg_marquardt_strategy.h" |
| #include "ceres/line_search_minimizer.h" |
| #include "ceres/linear_solver.h" |
| #include "ceres/map_util.h" |
| #include "ceres/minimizer.h" |
| #include "ceres/ordered_groups.h" |
| #include "ceres/parameter_block.h" |
| #include "ceres/parameter_block_ordering.h" |
| #include "ceres/preconditioner.h" |
| #include "ceres/problem.h" |
| #include "ceres/problem_impl.h" |
| #include "ceres/program.h" |
| #include "ceres/reorder_program.h" |
| #include "ceres/residual_block.h" |
| #include "ceres/stringprintf.h" |
| #include "ceres/suitesparse.h" |
| #include "ceres/summary_utils.h" |
| #include "ceres/trust_region_minimizer.h" |
| #include "ceres/wall_time.h" |
| |
| namespace ceres { |
| namespace internal { |
| |
| void SolverImpl::TrustRegionMinimize( |
| const Solver::Options& options, |
| Program* program, |
| CoordinateDescentMinimizer* inner_iteration_minimizer, |
| Evaluator* evaluator, |
| LinearSolver* linear_solver, |
| Solver::Summary* summary) { |
| Minimizer::Options minimizer_options(options); |
| minimizer_options.is_constrained = program->IsBoundsConstrained(); |
| |
| // The optimizer works on contiguous parameter vectors; allocate |
| // some. |
| Vector parameters(program->NumParameters()); |
| |
| // Collect the discontiguous parameters into a contiguous state |
| // vector. |
| program->ParameterBlocksToStateVector(parameters.data()); |
| |
| LoggingCallback logging_callback(TRUST_REGION, |
| options.minimizer_progress_to_stdout); |
| if (options.logging_type != SILENT) { |
| minimizer_options.callbacks.insert(minimizer_options.callbacks.begin(), |
| &logging_callback); |
| } |
| |
| StateUpdatingCallback updating_callback(program, parameters.data()); |
| if (options.update_state_every_iteration) { |
| // This must get pushed to the front of the callbacks so that it is run |
| // before any of the user callbacks. |
| minimizer_options.callbacks.insert(minimizer_options.callbacks.begin(), |
| &updating_callback); |
| } |
| |
| minimizer_options.evaluator = evaluator; |
| scoped_ptr<SparseMatrix> jacobian(evaluator->CreateJacobian()); |
| |
| minimizer_options.jacobian = jacobian.get(); |
| minimizer_options.inner_iteration_minimizer = inner_iteration_minimizer; |
| |
| TrustRegionStrategy::Options trust_region_strategy_options; |
| trust_region_strategy_options.linear_solver = linear_solver; |
| trust_region_strategy_options.initial_radius = |
| options.initial_trust_region_radius; |
| trust_region_strategy_options.max_radius = options.max_trust_region_radius; |
| trust_region_strategy_options.min_lm_diagonal = options.min_lm_diagonal; |
| trust_region_strategy_options.max_lm_diagonal = options.max_lm_diagonal; |
| trust_region_strategy_options.trust_region_strategy_type = |
| options.trust_region_strategy_type; |
| trust_region_strategy_options.dogleg_type = options.dogleg_type; |
| scoped_ptr<TrustRegionStrategy> strategy( |
| TrustRegionStrategy::Create(trust_region_strategy_options)); |
| minimizer_options.trust_region_strategy = strategy.get(); |
| |
| TrustRegionMinimizer minimizer; |
| double minimizer_start_time = WallTimeInSeconds(); |
| minimizer.Minimize(minimizer_options, parameters.data(), summary); |
| |
| // If the user aborted mid-optimization or the optimization |
| // terminated because of a numerical failure, then do not update |
| // user state. |
| if (summary->termination_type != USER_FAILURE && |
| summary->termination_type != FAILURE) { |
| program->StateVectorToParameterBlocks(parameters.data()); |
| program->CopyParameterBlockStateToUserState(); |
| } |
| |
| summary->minimizer_time_in_seconds = |
| WallTimeInSeconds() - minimizer_start_time; |
| } |
| |
| void SolverImpl::LineSearchMinimize( |
| const Solver::Options& options, |
| Program* program, |
| Evaluator* evaluator, |
| Solver::Summary* summary) { |
| Minimizer::Options minimizer_options(options); |
| |
| // The optimizer works on contiguous parameter vectors; allocate some. |
| Vector parameters(program->NumParameters()); |
| |
| // Collect the discontiguous parameters into a contiguous state vector. |
| program->ParameterBlocksToStateVector(parameters.data()); |
| |
| LoggingCallback logging_callback(LINE_SEARCH, |
| options.minimizer_progress_to_stdout); |
| if (options.logging_type != SILENT) { |
| minimizer_options.callbacks.insert(minimizer_options.callbacks.begin(), |
| &logging_callback); |
| } |
| |
| StateUpdatingCallback updating_callback(program, parameters.data()); |
| if (options.update_state_every_iteration) { |
| // This must get pushed to the front of the callbacks so that it is run |
| // before any of the user callbacks. |
| minimizer_options.callbacks.insert(minimizer_options.callbacks.begin(), |
| &updating_callback); |
| } |
| |
| minimizer_options.evaluator = evaluator; |
| |
| LineSearchMinimizer minimizer; |
| double minimizer_start_time = WallTimeInSeconds(); |
| minimizer.Minimize(minimizer_options, parameters.data(), summary); |
| |
| // If the user aborted mid-optimization or the optimization |
| // terminated because of a numerical failure, then do not update |
| // user state. |
| if (summary->termination_type != USER_FAILURE && |
| summary->termination_type != FAILURE) { |
| program->StateVectorToParameterBlocks(parameters.data()); |
| program->CopyParameterBlockStateToUserState(); |
| } |
| |
| summary->minimizer_time_in_seconds = |
| WallTimeInSeconds() - minimizer_start_time; |
| } |
| |
| void SolverImpl::Solve(const Solver::Options& options, |
| ProblemImpl* problem_impl, |
| Solver::Summary* summary) { |
| VLOG(2) << "Initial problem: " |
| << problem_impl->NumParameterBlocks() |
| << " parameter blocks, " |
| << problem_impl->NumParameters() |
| << " parameters, " |
| << problem_impl->NumResidualBlocks() |
| << " residual blocks, " |
| << problem_impl->NumResiduals() |
| << " residuals."; |
| if (options.minimizer_type == TRUST_REGION) { |
| TrustRegionSolve(options, problem_impl, summary); |
| } else { |
| LineSearchSolve(options, problem_impl, summary); |
| } |
| } |
| |
| void SolverImpl::TrustRegionSolve(const Solver::Options& original_options, |
| ProblemImpl* original_problem_impl, |
| Solver::Summary* summary) { |
| EventLogger event_logger("TrustRegionSolve"); |
| double solver_start_time = WallTimeInSeconds(); |
| |
| Program* original_program = original_problem_impl->mutable_program(); |
| ProblemImpl* problem_impl = original_problem_impl; |
| |
| summary->minimizer_type = TRUST_REGION; |
| |
| SummarizeGivenProgram(*original_program, summary); |
| OrderingToGroupSizes(original_options.linear_solver_ordering.get(), |
| &(summary->linear_solver_ordering_given)); |
| OrderingToGroupSizes(original_options.inner_iteration_ordering.get(), |
| &(summary->inner_iteration_ordering_given)); |
| |
| Solver::Options options(original_options); |
| |
| #ifndef CERES_USE_OPENMP |
| if (options.num_threads > 1) { |
| LOG(WARNING) |
| << "OpenMP support is not compiled into this binary; " |
| << "only options.num_threads=1 is supported. Switching " |
| << "to single threaded mode."; |
| options.num_threads = 1; |
| } |
| if (options.num_linear_solver_threads > 1) { |
| LOG(WARNING) |
| << "OpenMP support is not compiled into this binary; " |
| << "only options.num_linear_solver_threads=1 is supported. Switching " |
| << "to single threaded mode."; |
| options.num_linear_solver_threads = 1; |
| } |
| #endif |
| |
| summary->num_threads_given = original_options.num_threads; |
| summary->num_threads_used = options.num_threads; |
| |
| if (options.trust_region_minimizer_iterations_to_dump.size() > 0 && |
| options.trust_region_problem_dump_format_type != CONSOLE && |
| options.trust_region_problem_dump_directory.empty()) { |
| summary->message = |
| "Solver::Options::trust_region_problem_dump_directory is empty."; |
| LOG(ERROR) << summary->message; |
| return; |
| } |
| |
| if (!original_program->ParameterBlocksAreFinite(&summary->message)) { |
| LOG(ERROR) << "Terminating: " << summary->message; |
| return; |
| } |
| |
| if (!original_program->IsFeasible(&summary->message)) { |
| LOG(ERROR) << "Terminating: " << summary->message; |
| return; |
| } |
| |
| event_logger.AddEvent("Init"); |
| |
| original_program->SetParameterBlockStatePtrsToUserStatePtrs(); |
| event_logger.AddEvent("SetParameterBlockPtrs"); |
| |
| // If the user requests gradient checking, construct a new |
| // ProblemImpl by wrapping the CostFunctions of problem_impl inside |
| // GradientCheckingCostFunction and replacing problem_impl with |
| // gradient_checking_problem_impl. |
| scoped_ptr<ProblemImpl> gradient_checking_problem_impl; |
| if (options.check_gradients) { |
| VLOG(1) << "Checking Gradients"; |
| gradient_checking_problem_impl.reset( |
| CreateGradientCheckingProblemImpl( |
| problem_impl, |
| options.numeric_derivative_relative_step_size, |
| options.gradient_check_relative_precision)); |
| |
| // From here on, problem_impl will point to the gradient checking |
| // version. |
| problem_impl = gradient_checking_problem_impl.get(); |
| } |
| |
| if (options.linear_solver_ordering.get() != NULL) { |
| if (!IsOrderingValid(options, problem_impl, &summary->message)) { |
| LOG(ERROR) << summary->message; |
| return; |
| } |
| event_logger.AddEvent("CheckOrdering"); |
| } else { |
| options.linear_solver_ordering.reset(new ParameterBlockOrdering); |
| const ProblemImpl::ParameterMap& parameter_map = |
| problem_impl->parameter_map(); |
| for (ProblemImpl::ParameterMap::const_iterator it = parameter_map.begin(); |
| it != parameter_map.end(); |
| ++it) { |
| options.linear_solver_ordering->AddElementToGroup(it->first, 0); |
| } |
| event_logger.AddEvent("ConstructOrdering"); |
| } |
| |
| // Create the three objects needed to minimize: the transformed program, the |
| // evaluator, and the linear solver. |
| scoped_ptr<Program> reduced_program(CreateReducedProgram(&options, |
| problem_impl, |
| &summary->fixed_cost, |
| &summary->message)); |
| |
| event_logger.AddEvent("CreateReducedProgram"); |
| if (reduced_program == NULL) { |
| return; |
| } |
| |
| OrderingToGroupSizes(options.linear_solver_ordering.get(), |
| &(summary->linear_solver_ordering_used)); |
| SummarizeReducedProgram(*reduced_program, summary); |
| |
| if (summary->num_parameter_blocks_reduced == 0) { |
| summary->preprocessor_time_in_seconds = |
| WallTimeInSeconds() - solver_start_time; |
| |
| double post_process_start_time = WallTimeInSeconds(); |
| |
| summary->message = |
| "Function tolerance reached. " |
| "No non-constant parameter blocks found."; |
| summary->termination_type = CONVERGENCE; |
| VLOG_IF(1, options.logging_type != SILENT) << summary->message; |
| |
| summary->initial_cost = summary->fixed_cost; |
| summary->final_cost = summary->fixed_cost; |
| |
| // Ensure the program state is set to the user parameters on the way out. |
| original_program->SetParameterBlockStatePtrsToUserStatePtrs(); |
| original_program->SetParameterOffsetsAndIndex(); |
| |
| summary->postprocessor_time_in_seconds = |
| WallTimeInSeconds() - post_process_start_time; |
| return; |
| } |
| |
| scoped_ptr<LinearSolver> |
| linear_solver(CreateLinearSolver(&options, &summary->message)); |
| event_logger.AddEvent("CreateLinearSolver"); |
| if (linear_solver == NULL) { |
| return; |
| } |
| |
| summary->linear_solver_type_given = original_options.linear_solver_type; |
| summary->linear_solver_type_used = options.linear_solver_type; |
| |
| summary->preconditioner_type = options.preconditioner_type; |
| summary->visibility_clustering_type = options.visibility_clustering_type; |
| |
| summary->num_linear_solver_threads_given = |
| original_options.num_linear_solver_threads; |
| summary->num_linear_solver_threads_used = options.num_linear_solver_threads; |
| |
| summary->dense_linear_algebra_library_type = |
| options.dense_linear_algebra_library_type; |
| summary->sparse_linear_algebra_library_type = |
| options.sparse_linear_algebra_library_type; |
| |
| summary->trust_region_strategy_type = options.trust_region_strategy_type; |
| summary->dogleg_type = options.dogleg_type; |
| |
| scoped_ptr<Evaluator> evaluator(CreateEvaluator(options, |
| problem_impl->parameter_map(), |
| reduced_program.get(), |
| &summary->message)); |
| |
| event_logger.AddEvent("CreateEvaluator"); |
| |
| if (evaluator == NULL) { |
| return; |
| } |
| |
| scoped_ptr<CoordinateDescentMinimizer> inner_iteration_minimizer; |
| if (options.use_inner_iterations) { |
| if (reduced_program->parameter_blocks().size() < 2) { |
| LOG(WARNING) << "Reduced problem only contains one parameter block." |
| << "Disabling inner iterations."; |
| } else { |
| inner_iteration_minimizer.reset( |
| CreateInnerIterationMinimizer(options, |
| *reduced_program, |
| problem_impl->parameter_map(), |
| summary)); |
| if (inner_iteration_minimizer == NULL) { |
| LOG(ERROR) << summary->message; |
| return; |
| } |
| } |
| } |
| event_logger.AddEvent("CreateInnerIterationMinimizer"); |
| |
| double minimizer_start_time = WallTimeInSeconds(); |
| summary->preprocessor_time_in_seconds = |
| minimizer_start_time - solver_start_time; |
| |
| // Run the optimization. |
| TrustRegionMinimize(options, |
| reduced_program.get(), |
| inner_iteration_minimizer.get(), |
| evaluator.get(), |
| linear_solver.get(), |
| summary); |
| event_logger.AddEvent("Minimize"); |
| |
| double post_process_start_time = WallTimeInSeconds(); |
| |
| SetSummaryFinalCost(summary); |
| |
| // Ensure the program state is set to the user parameters on the way |
| // out. |
| original_program->SetParameterBlockStatePtrsToUserStatePtrs(); |
| original_program->SetParameterOffsetsAndIndex(); |
| |
| const map<string, double>& linear_solver_time_statistics = |
| linear_solver->TimeStatistics(); |
| summary->linear_solver_time_in_seconds = |
| FindWithDefault(linear_solver_time_statistics, |
| "LinearSolver::Solve", |
| 0.0); |
| |
| const map<string, double>& evaluator_time_statistics = |
| evaluator->TimeStatistics(); |
| |
| summary->residual_evaluation_time_in_seconds = |
| FindWithDefault(evaluator_time_statistics, "Evaluator::Residual", 0.0); |
| summary->jacobian_evaluation_time_in_seconds = |
| FindWithDefault(evaluator_time_statistics, "Evaluator::Jacobian", 0.0); |
| |
| // Stick a fork in it, we're done. |
| summary->postprocessor_time_in_seconds = |
| WallTimeInSeconds() - post_process_start_time; |
| event_logger.AddEvent("PostProcess"); |
| } |
| |
| void SolverImpl::LineSearchSolve(const Solver::Options& original_options, |
| ProblemImpl* original_problem_impl, |
| Solver::Summary* summary) { |
| double solver_start_time = WallTimeInSeconds(); |
| |
| Program* original_program = original_problem_impl->mutable_program(); |
| ProblemImpl* problem_impl = original_problem_impl; |
| |
| SummarizeGivenProgram(*original_program, summary); |
| summary->minimizer_type = LINE_SEARCH; |
| summary->line_search_direction_type = |
| original_options.line_search_direction_type; |
| summary->max_lbfgs_rank = original_options.max_lbfgs_rank; |
| summary->line_search_type = original_options.line_search_type; |
| summary->line_search_interpolation_type = |
| original_options.line_search_interpolation_type; |
| summary->nonlinear_conjugate_gradient_type = |
| original_options.nonlinear_conjugate_gradient_type; |
| |
| if (original_program->IsBoundsConstrained()) { |
| summary->message = "LINE_SEARCH Minimizer does not support bounds."; |
| LOG(ERROR) << "Terminating: " << summary->message; |
| return; |
| } |
| |
| Solver::Options options(original_options); |
| |
| // This ensures that we get a Block Jacobian Evaluator along with |
| // none of the Schur nonsense. This file will have to be extensively |
| // refactored to deal with the various bits of cleanups related to |
| // line search. |
| options.linear_solver_type = CGNR; |
| |
| |
| #ifndef CERES_USE_OPENMP |
| if (options.num_threads > 1) { |
| LOG(WARNING) |
| << "OpenMP support is not compiled into this binary; " |
| << "only options.num_threads=1 is supported. Switching " |
| << "to single threaded mode."; |
| options.num_threads = 1; |
| } |
| #endif // CERES_USE_OPENMP |
| |
| summary->num_threads_given = original_options.num_threads; |
| summary->num_threads_used = options.num_threads; |
| |
| if (!original_program->ParameterBlocksAreFinite(&summary->message)) { |
| LOG(ERROR) << "Terminating: " << summary->message; |
| return; |
| } |
| |
| if (options.linear_solver_ordering.get() != NULL) { |
| if (!IsOrderingValid(options, problem_impl, &summary->message)) { |
| LOG(ERROR) << summary->message; |
| return; |
| } |
| } else { |
| options.linear_solver_ordering.reset(new ParameterBlockOrdering); |
| const ProblemImpl::ParameterMap& parameter_map = |
| problem_impl->parameter_map(); |
| for (ProblemImpl::ParameterMap::const_iterator it = parameter_map.begin(); |
| it != parameter_map.end(); |
| ++it) { |
| options.linear_solver_ordering->AddElementToGroup(it->first, 0); |
| } |
| } |
| |
| |
| original_program->SetParameterBlockStatePtrsToUserStatePtrs(); |
| |
| // If the user requests gradient checking, construct a new |
| // ProblemImpl by wrapping the CostFunctions of problem_impl inside |
| // GradientCheckingCostFunction and replacing problem_impl with |
| // gradient_checking_problem_impl. |
| scoped_ptr<ProblemImpl> gradient_checking_problem_impl; |
| if (options.check_gradients) { |
| VLOG(1) << "Checking Gradients"; |
| gradient_checking_problem_impl.reset( |
| CreateGradientCheckingProblemImpl( |
| problem_impl, |
| options.numeric_derivative_relative_step_size, |
| options.gradient_check_relative_precision)); |
| |
| // From here on, problem_impl will point to the gradient checking |
| // version. |
| problem_impl = gradient_checking_problem_impl.get(); |
| } |
| |
| // Create the three objects needed to minimize: the transformed program, the |
| // evaluator, and the linear solver. |
| scoped_ptr<Program> reduced_program(CreateReducedProgram(&options, |
| problem_impl, |
| &summary->fixed_cost, |
| &summary->message)); |
| if (reduced_program == NULL) { |
| return; |
| } |
| |
| SummarizeReducedProgram(*reduced_program, summary); |
| if (summary->num_parameter_blocks_reduced == 0) { |
| summary->preprocessor_time_in_seconds = |
| WallTimeInSeconds() - solver_start_time; |
| |
| summary->message = |
| "Function tolerance reached. " |
| "No non-constant parameter blocks found."; |
| summary->termination_type = CONVERGENCE; |
| VLOG_IF(1, options.logging_type != SILENT) << summary->message; |
| summary->initial_cost = summary->fixed_cost; |
| summary->final_cost = summary->fixed_cost; |
| |
| const double post_process_start_time = WallTimeInSeconds(); |
| SetSummaryFinalCost(summary); |
| |
| // Ensure the program state is set to the user parameters on the way out. |
| original_program->SetParameterBlockStatePtrsToUserStatePtrs(); |
| original_program->SetParameterOffsetsAndIndex(); |
| |
| summary->postprocessor_time_in_seconds = |
| WallTimeInSeconds() - post_process_start_time; |
| return; |
| } |
| |
| scoped_ptr<Evaluator> evaluator(CreateEvaluator(options, |
| problem_impl->parameter_map(), |
| reduced_program.get(), |
| &summary->message)); |
| if (evaluator == NULL) { |
| return; |
| } |
| |
| const double minimizer_start_time = WallTimeInSeconds(); |
| summary->preprocessor_time_in_seconds = |
| minimizer_start_time - solver_start_time; |
| |
| // Run the optimization. |
| LineSearchMinimize(options, reduced_program.get(), evaluator.get(), summary); |
| |
| const double post_process_start_time = WallTimeInSeconds(); |
| |
| SetSummaryFinalCost(summary); |
| |
| // Ensure the program state is set to the user parameters on the way out. |
| original_program->SetParameterBlockStatePtrsToUserStatePtrs(); |
| original_program->SetParameterOffsetsAndIndex(); |
| |
| const map<string, double>& evaluator_time_statistics = |
| evaluator->TimeStatistics(); |
| |
| summary->residual_evaluation_time_in_seconds = |
| FindWithDefault(evaluator_time_statistics, "Evaluator::Residual", 0.0); |
| summary->jacobian_evaluation_time_in_seconds = |
| FindWithDefault(evaluator_time_statistics, "Evaluator::Jacobian", 0.0); |
| |
| // Stick a fork in it, we're done. |
| summary->postprocessor_time_in_seconds = |
| WallTimeInSeconds() - post_process_start_time; |
| } |
| |
| bool SolverImpl::IsOrderingValid(const Solver::Options& options, |
| const ProblemImpl* problem_impl, |
| string* error) { |
| if (options.linear_solver_ordering->NumElements() != |
| problem_impl->NumParameterBlocks()) { |
| *error = "Number of parameter blocks in user supplied ordering " |
| "does not match the number of parameter blocks in the problem"; |
| return false; |
| } |
| |
| const Program& program = problem_impl->program(); |
| const vector<ParameterBlock*>& parameter_blocks = program.parameter_blocks(); |
| for (vector<ParameterBlock*>::const_iterator it = parameter_blocks.begin(); |
| it != parameter_blocks.end(); |
| ++it) { |
| if (!options.linear_solver_ordering |
| ->IsMember(const_cast<double*>((*it)->user_state()))) { |
| *error = "Problem contains a parameter block that is not in " |
| "the user specified ordering."; |
| return false; |
| } |
| } |
| |
| if (IsSchurType(options.linear_solver_type) && |
| options.linear_solver_ordering->NumGroups() > 1) { |
| const vector<ResidualBlock*>& residual_blocks = program.residual_blocks(); |
| const set<double*>& e_blocks = |
| options.linear_solver_ordering->group_to_elements().begin()->second; |
| if (!IsParameterBlockSetIndependent(e_blocks, residual_blocks)) { |
| *error = "The user requested the use of a Schur type solver. " |
| "But the first elimination group in the ordering is not an " |
| "independent set."; |
| return false; |
| } |
| } |
| return true; |
| } |
| |
| bool SolverImpl::IsParameterBlockSetIndependent( |
| const set<double*>& parameter_block_ptrs, |
| const vector<ResidualBlock*>& residual_blocks) { |
| // Loop over each residual block and ensure that no two parameter |
| // blocks in the same residual block are part of |
| // parameter_block_ptrs as that would violate the assumption that it |
| // is an independent set in the Hessian matrix. |
| for (vector<ResidualBlock*>::const_iterator it = residual_blocks.begin(); |
| it != residual_blocks.end(); |
| ++it) { |
| ParameterBlock* const* parameter_blocks = (*it)->parameter_blocks(); |
| const int num_parameter_blocks = (*it)->NumParameterBlocks(); |
| int count = 0; |
| for (int i = 0; i < num_parameter_blocks; ++i) { |
| count += parameter_block_ptrs.count( |
| parameter_blocks[i]->mutable_user_state()); |
| } |
| if (count > 1) { |
| return false; |
| } |
| } |
| return true; |
| } |
| |
| Program* SolverImpl::CreateReducedProgram(Solver::Options* options, |
| ProblemImpl* problem_impl, |
| double* fixed_cost, |
| string* error) { |
| CHECK_NOTNULL(options->linear_solver_ordering.get()); |
| Program* original_program = problem_impl->mutable_program(); |
| |
| vector<double*> removed_parameter_blocks; |
| scoped_ptr<Program> reduced_program( |
| original_program->CreateReducedProgram(&removed_parameter_blocks, |
| fixed_cost, |
| error)); |
| if (reduced_program.get() == NULL) { |
| return NULL; |
| } |
| |
| VLOG(2) << "Reduced problem: " |
| << reduced_program->NumParameterBlocks() |
| << " parameter blocks, " |
| << reduced_program->NumParameters() |
| << " parameters, " |
| << reduced_program->NumResidualBlocks() |
| << " residual blocks, " |
| << reduced_program->NumResiduals() |
| << " residuals."; |
| |
| if (reduced_program->NumParameterBlocks() == 0) { |
| LOG(WARNING) << "No varying parameter blocks to optimize; " |
| << "bailing early."; |
| return reduced_program.release(); |
| } |
| |
| ParameterBlockOrdering* linear_solver_ordering = |
| options->linear_solver_ordering.get(); |
| const int min_group_id = |
| linear_solver_ordering->MinNonZeroGroup(); |
| linear_solver_ordering->Remove(removed_parameter_blocks); |
| |
| ParameterBlockOrdering* inner_iteration_ordering = |
| options->inner_iteration_ordering.get(); |
| if (inner_iteration_ordering != NULL) { |
| inner_iteration_ordering->Remove(removed_parameter_blocks); |
| } |
| |
| if (IsSchurType(options->linear_solver_type) && |
| linear_solver_ordering->GroupSize(min_group_id) == 0) { |
| // If the user requested the use of a Schur type solver, and |
| // supplied a non-NULL linear_solver_ordering object with more than |
| // one elimination group, then it can happen that after all the |
| // parameter blocks which are fixed or unused have been removed from |
| // the program and the ordering, there are no more parameter blocks |
| // in the first elimination group. |
| // |
| // In such a case, the use of a Schur type solver is not possible, |
| // as they assume there is at least one e_block. Thus, we |
| // automatically switch to the closest solver to the one indicated |
| // by the user. |
| if (options->linear_solver_type == ITERATIVE_SCHUR) { |
| options->preconditioner_type = |
| Preconditioner::PreconditionerForZeroEBlocks( |
| options->preconditioner_type); |
| } |
| |
| options->linear_solver_type = |
| LinearSolver::LinearSolverForZeroEBlocks( |
| options->linear_solver_type); |
| } |
| |
| if (IsSchurType(options->linear_solver_type)) { |
| if (!ReorderProgramForSchurTypeLinearSolver( |
| options->linear_solver_type, |
| options->sparse_linear_algebra_library_type, |
| problem_impl->parameter_map(), |
| linear_solver_ordering, |
| reduced_program.get(), |
| error)) { |
| return NULL; |
| } |
| return reduced_program.release(); |
| } |
| |
| if (options->linear_solver_type == SPARSE_NORMAL_CHOLESKY && |
| !options->dynamic_sparsity) { |
| if (!ReorderProgramForSparseNormalCholesky( |
| options->sparse_linear_algebra_library_type, |
| *linear_solver_ordering, |
| reduced_program.get(), |
| error)) { |
| return NULL; |
| } |
| |
| return reduced_program.release(); |
| } |
| |
| reduced_program->SetParameterOffsetsAndIndex(); |
| return reduced_program.release(); |
| } |
| |
| LinearSolver* SolverImpl::CreateLinearSolver(Solver::Options* options, |
| string* error) { |
| CHECK_NOTNULL(options); |
| CHECK_NOTNULL(options->linear_solver_ordering.get()); |
| CHECK_NOTNULL(error); |
| |
| if (options->trust_region_strategy_type == DOGLEG) { |
| if (options->linear_solver_type == ITERATIVE_SCHUR || |
| options->linear_solver_type == CGNR) { |
| *error = "DOGLEG only supports exact factorization based linear " |
| "solvers. If you want to use an iterative solver please " |
| "use LEVENBERG_MARQUARDT as the trust_region_strategy_type"; |
| return NULL; |
| } |
| } |
| |
| #ifdef CERES_NO_LAPACK |
| if (options->linear_solver_type == DENSE_NORMAL_CHOLESKY && |
| options->dense_linear_algebra_library_type == LAPACK) { |
| *error = "Can't use DENSE_NORMAL_CHOLESKY with LAPACK because " |
| "LAPACK was not enabled when Ceres was built."; |
| return NULL; |
| } |
| |
| if (options->linear_solver_type == DENSE_QR && |
| options->dense_linear_algebra_library_type == LAPACK) { |
| *error = "Can't use DENSE_QR with LAPACK because " |
| "LAPACK was not enabled when Ceres was built."; |
| return NULL; |
| } |
| |
| if (options->linear_solver_type == DENSE_SCHUR && |
| options->dense_linear_algebra_library_type == LAPACK) { |
| *error = "Can't use DENSE_SCHUR with LAPACK because " |
| "LAPACK was not enabled when Ceres was built."; |
| return NULL; |
| } |
| #endif |
| |
| #ifdef CERES_NO_SUITESPARSE |
| if (options->linear_solver_type == SPARSE_NORMAL_CHOLESKY && |
| options->sparse_linear_algebra_library_type == SUITE_SPARSE) { |
| *error = "Can't use SPARSE_NORMAL_CHOLESKY with SUITESPARSE because " |
| "SuiteSparse was not enabled when Ceres was built."; |
| return NULL; |
| } |
| |
| if (options->preconditioner_type == CLUSTER_JACOBI) { |
| *error = "CLUSTER_JACOBI preconditioner not suppored. Please build Ceres " |
| "with SuiteSparse support."; |
| return NULL; |
| } |
| |
| if (options->preconditioner_type == CLUSTER_TRIDIAGONAL) { |
| *error = "CLUSTER_TRIDIAGONAL preconditioner not suppored. Please build " |
| "Ceres with SuiteSparse support."; |
| return NULL; |
| } |
| #endif |
| |
| #ifdef CERES_NO_CXSPARSE |
| if (options->linear_solver_type == SPARSE_NORMAL_CHOLESKY && |
| options->sparse_linear_algebra_library_type == CX_SPARSE) { |
| *error = "Can't use SPARSE_NORMAL_CHOLESKY with CXSPARSE because " |
| "CXSparse was not enabled when Ceres was built."; |
| return NULL; |
| } |
| #endif |
| |
| if (options->max_linear_solver_iterations <= 0) { |
| *error = "Solver::Options::max_linear_solver_iterations is not positive."; |
| return NULL; |
| } |
| if (options->min_linear_solver_iterations <= 0) { |
| *error = "Solver::Options::min_linear_solver_iterations is not positive."; |
| return NULL; |
| } |
| if (options->min_linear_solver_iterations > |
| options->max_linear_solver_iterations) { |
| *error = "Solver::Options::min_linear_solver_iterations > " |
| "Solver::Options::max_linear_solver_iterations."; |
| return NULL; |
| } |
| |
| LinearSolver::Options linear_solver_options; |
| linear_solver_options.min_num_iterations = |
| options->min_linear_solver_iterations; |
| linear_solver_options.max_num_iterations = |
| options->max_linear_solver_iterations; |
| linear_solver_options.type = options->linear_solver_type; |
| linear_solver_options.preconditioner_type = options->preconditioner_type; |
| linear_solver_options.visibility_clustering_type = |
| options->visibility_clustering_type; |
| linear_solver_options.sparse_linear_algebra_library_type = |
| options->sparse_linear_algebra_library_type; |
| linear_solver_options.dense_linear_algebra_library_type = |
| options->dense_linear_algebra_library_type; |
| linear_solver_options.use_postordering = options->use_postordering; |
| linear_solver_options.dynamic_sparsity = options->dynamic_sparsity; |
| |
| // Ignore user's postordering preferences and force it to be true if |
| // cholmod_camd is not available. This ensures that the linear |
| // solver does not assume that a fill-reducing pre-ordering has been |
| // done. |
| #if !defined(CERES_NO_SUITESPARSE) && defined(CERES_NO_CAMD) |
| if (IsSchurType(linear_solver_options.type) && |
| options->sparse_linear_algebra_library_type == SUITE_SPARSE) { |
| linear_solver_options.use_postordering = true; |
| } |
| #endif |
| |
| linear_solver_options.num_threads = options->num_linear_solver_threads; |
| options->num_linear_solver_threads = linear_solver_options.num_threads; |
| |
| OrderingToGroupSizes(options->linear_solver_ordering.get(), |
| &linear_solver_options.elimination_groups); |
| // Schur type solvers, expect at least two elimination groups. If |
| // there is only one elimination group, then CreateReducedProgram |
| // guarantees that this group only contains e_blocks. Thus we add a |
| // dummy elimination group with zero blocks in it. |
| if (IsSchurType(linear_solver_options.type) && |
| linear_solver_options.elimination_groups.size() == 1) { |
| linear_solver_options.elimination_groups.push_back(0); |
| } |
| |
| return LinearSolver::Create(linear_solver_options); |
| } |
| |
| Evaluator* SolverImpl::CreateEvaluator( |
| const Solver::Options& options, |
| const ProblemImpl::ParameterMap& parameter_map, |
| Program* program, |
| string* error) { |
| Evaluator::Options evaluator_options; |
| evaluator_options.linear_solver_type = options.linear_solver_type; |
| evaluator_options.num_eliminate_blocks = |
| (options.linear_solver_ordering->NumGroups() > 0 && |
| IsSchurType(options.linear_solver_type)) |
| ? (options.linear_solver_ordering |
| ->group_to_elements().begin() |
| ->second.size()) |
| : 0; |
| evaluator_options.num_threads = options.num_threads; |
| evaluator_options.dynamic_sparsity = options.dynamic_sparsity; |
| return Evaluator::Create(evaluator_options, program, error); |
| } |
| |
| CoordinateDescentMinimizer* SolverImpl::CreateInnerIterationMinimizer( |
| const Solver::Options& options, |
| const Program& program, |
| const ProblemImpl::ParameterMap& parameter_map, |
| Solver::Summary* summary) { |
| summary->inner_iterations_given = true; |
| |
| scoped_ptr<CoordinateDescentMinimizer> inner_iteration_minimizer( |
| new CoordinateDescentMinimizer); |
| scoped_ptr<ParameterBlockOrdering> inner_iteration_ordering; |
| ParameterBlockOrdering* ordering_ptr = NULL; |
| |
| if (options.inner_iteration_ordering.get() == NULL) { |
| inner_iteration_ordering.reset( |
| CoordinateDescentMinimizer::CreateOrdering(program)); |
| ordering_ptr = inner_iteration_ordering.get(); |
| } else { |
| ordering_ptr = options.inner_iteration_ordering.get(); |
| if (!CoordinateDescentMinimizer::IsOrderingValid(program, |
| *ordering_ptr, |
| &summary->message)) { |
| return NULL; |
| } |
| } |
| |
| if (!inner_iteration_minimizer->Init(program, |
| parameter_map, |
| *ordering_ptr, |
| &summary->message)) { |
| return NULL; |
| } |
| |
| summary->inner_iterations_used = true; |
| summary->inner_iteration_time_in_seconds = 0.0; |
| OrderingToGroupSizes(ordering_ptr, |
| &(summary->inner_iteration_ordering_used)); |
| return inner_iteration_minimizer.release(); |
| } |
| |
| } // namespace internal |
| } // namespace ceres |
