279 lines
10 KiB
C++
279 lines
10 KiB
C++
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// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2015 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: sameeragarwal@google.com (Sameer Agarwal)
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#include "ceres/coordinate_descent_minimizer.h"
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#ifdef CERES_USE_OPENMP
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#include <omp.h>
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#endif
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#include <iterator>
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#include <numeric>
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#include <vector>
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#include "ceres/evaluator.h"
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#include "ceres/linear_solver.h"
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#include "ceres/minimizer.h"
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#include "ceres/parameter_block.h"
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#include "ceres/parameter_block_ordering.h"
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#include "ceres/problem_impl.h"
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#include "ceres/program.h"
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#include "ceres/residual_block.h"
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#include "ceres/solver.h"
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#include "ceres/trust_region_minimizer.h"
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#include "ceres/trust_region_strategy.h"
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namespace ceres {
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namespace internal {
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using std::map;
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using std::max;
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using std::min;
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using std::set;
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using std::string;
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using std::vector;
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CoordinateDescentMinimizer::~CoordinateDescentMinimizer() {
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}
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bool CoordinateDescentMinimizer::Init(
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const Program& program,
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const ProblemImpl::ParameterMap& parameter_map,
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const ParameterBlockOrdering& ordering,
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string* error) {
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parameter_blocks_.clear();
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independent_set_offsets_.clear();
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independent_set_offsets_.push_back(0);
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// Serialize the OrderedGroups into a vector of parameter block
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// offsets for parallel access.
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map<ParameterBlock*, int> parameter_block_index;
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map<int, set<double*> > group_to_elements = ordering.group_to_elements();
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for (map<int, set<double*> >::const_iterator it = group_to_elements.begin();
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it != group_to_elements.end();
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++it) {
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for (set<double*>::const_iterator ptr_it = it->second.begin();
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ptr_it != it->second.end();
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++ptr_it) {
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parameter_blocks_.push_back(parameter_map.find(*ptr_it)->second);
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parameter_block_index[parameter_blocks_.back()] =
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parameter_blocks_.size() - 1;
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}
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independent_set_offsets_.push_back(
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independent_set_offsets_.back() + it->second.size());
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}
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// The ordering does not have to contain all parameter blocks, so
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// assign zero offsets/empty independent sets to these parameter
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// blocks.
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const vector<ParameterBlock*>& parameter_blocks = program.parameter_blocks();
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for (int i = 0; i < parameter_blocks.size(); ++i) {
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if (!ordering.IsMember(parameter_blocks[i]->mutable_user_state())) {
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parameter_blocks_.push_back(parameter_blocks[i]);
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independent_set_offsets_.push_back(independent_set_offsets_.back());
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}
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}
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// Compute the set of residual blocks that depend on each parameter
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// block.
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residual_blocks_.resize(parameter_block_index.size());
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const vector<ResidualBlock*>& residual_blocks = program.residual_blocks();
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for (int i = 0; i < residual_blocks.size(); ++i) {
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ResidualBlock* residual_block = residual_blocks[i];
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const int num_parameter_blocks = residual_block->NumParameterBlocks();
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for (int j = 0; j < num_parameter_blocks; ++j) {
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ParameterBlock* parameter_block = residual_block->parameter_blocks()[j];
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const map<ParameterBlock*, int>::const_iterator it =
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parameter_block_index.find(parameter_block);
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if (it != parameter_block_index.end()) {
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residual_blocks_[it->second].push_back(residual_block);
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}
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}
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}
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evaluator_options_.linear_solver_type = DENSE_QR;
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evaluator_options_.num_eliminate_blocks = 0;
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evaluator_options_.num_threads = 1;
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return true;
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}
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void CoordinateDescentMinimizer::Minimize(
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const Minimizer::Options& options,
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double* parameters,
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Solver::Summary* summary) {
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// Set the state and mark all parameter blocks constant.
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for (int i = 0; i < parameter_blocks_.size(); ++i) {
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ParameterBlock* parameter_block = parameter_blocks_[i];
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parameter_block->SetState(parameters + parameter_block->state_offset());
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parameter_block->SetConstant();
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}
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scoped_array<LinearSolver*> linear_solvers(
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new LinearSolver*[options.num_threads]);
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LinearSolver::Options linear_solver_options;
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linear_solver_options.type = DENSE_QR;
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for (int i = 0; i < options.num_threads; ++i) {
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linear_solvers[i] = LinearSolver::Create(linear_solver_options);
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}
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for (int i = 0; i < independent_set_offsets_.size() - 1; ++i) {
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const int num_problems =
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independent_set_offsets_[i + 1] - independent_set_offsets_[i];
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// No point paying the price for an OpemMP call if the set is of
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// size zero.
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if (num_problems == 0) {
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continue;
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}
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#ifdef CERES_USE_OPENMP
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const int num_inner_iteration_threads =
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min(options.num_threads, num_problems);
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evaluator_options_.num_threads =
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max(1, options.num_threads / num_inner_iteration_threads);
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// The parameter blocks in each independent set can be optimized
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// in parallel, since they do not co-occur in any residual block.
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#pragma omp parallel for num_threads(num_inner_iteration_threads)
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#endif
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for (int j = independent_set_offsets_[i];
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j < independent_set_offsets_[i + 1];
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++j) {
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#ifdef CERES_USE_OPENMP
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int thread_id = omp_get_thread_num();
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#else
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int thread_id = 0;
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#endif
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ParameterBlock* parameter_block = parameter_blocks_[j];
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const int old_index = parameter_block->index();
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const int old_delta_offset = parameter_block->delta_offset();
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parameter_block->SetVarying();
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parameter_block->set_index(0);
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parameter_block->set_delta_offset(0);
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Program inner_program;
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inner_program.mutable_parameter_blocks()->push_back(parameter_block);
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*inner_program.mutable_residual_blocks() = residual_blocks_[j];
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// TODO(sameeragarwal): Better error handling. Right now we
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// assume that this is not going to lead to problems of any
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// sort. Basically we should be checking for numerical failure
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// of some sort.
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//
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// On the other hand, if the optimization is a failure, that in
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// some ways is fine, since it won't change the parameters and
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// we are fine.
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Solver::Summary inner_summary;
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Solve(&inner_program,
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linear_solvers[thread_id],
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parameters + parameter_block->state_offset(),
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&inner_summary);
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parameter_block->set_index(old_index);
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parameter_block->set_delta_offset(old_delta_offset);
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parameter_block->SetState(parameters + parameter_block->state_offset());
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parameter_block->SetConstant();
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}
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}
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for (int i = 0; i < parameter_blocks_.size(); ++i) {
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parameter_blocks_[i]->SetVarying();
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}
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for (int i = 0; i < options.num_threads; ++i) {
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delete linear_solvers[i];
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}
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}
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// Solve the optimization problem for one parameter block.
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void CoordinateDescentMinimizer::Solve(Program* program,
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LinearSolver* linear_solver,
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double* parameter,
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Solver::Summary* summary) {
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*summary = Solver::Summary();
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summary->initial_cost = 0.0;
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summary->fixed_cost = 0.0;
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summary->final_cost = 0.0;
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string error;
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Minimizer::Options minimizer_options;
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minimizer_options.evaluator.reset(
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CHECK_NOTNULL(Evaluator::Create(evaluator_options_, program, &error)));
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minimizer_options.jacobian.reset(
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CHECK_NOTNULL(minimizer_options.evaluator->CreateJacobian()));
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TrustRegionStrategy::Options trs_options;
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trs_options.linear_solver = linear_solver;
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minimizer_options.trust_region_strategy.reset(
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CHECK_NOTNULL(TrustRegionStrategy::Create(trs_options)));
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minimizer_options.is_silent = true;
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TrustRegionMinimizer minimizer;
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minimizer.Minimize(minimizer_options, parameter, summary);
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}
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bool CoordinateDescentMinimizer::IsOrderingValid(
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const Program& program,
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const ParameterBlockOrdering& ordering,
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string* message) {
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const map<int, set<double*> >& group_to_elements =
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ordering.group_to_elements();
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// Verify that each group is an independent set
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map<int, set<double*> >::const_iterator it = group_to_elements.begin();
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for (; it != group_to_elements.end(); ++it) {
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if (!program.IsParameterBlockSetIndependent(it->second)) {
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*message =
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StringPrintf("The user-provided "
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"parameter_blocks_for_inner_iterations does not "
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"form an independent set. Group Id: %d", it->first);
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return false;
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}
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}
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return true;
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}
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// Find a recursive decomposition of the Hessian matrix as a set
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// of independent sets of decreasing size and invert it. This
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// seems to work better in practice, i.e., Cameras before
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// points.
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ParameterBlockOrdering* CoordinateDescentMinimizer::CreateOrdering(
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const Program& program) {
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scoped_ptr<ParameterBlockOrdering> ordering(new ParameterBlockOrdering);
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ComputeRecursiveIndependentSetOrdering(program, ordering.get());
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ordering->Reverse();
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return ordering.release();
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}
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} // namespace internal
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} // namespace ceres
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