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https://github.com/ceres-solver/ceres-solver.git
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b9f15a5936
For problems with a small number of variables, but a large number of residuals, it is sometimes beneficial to use the Cholesky factorization on the normal equations, instead of the dense QR factorization of the Jacobian, even though it is numerically the better thing to do. Change-Id: I3506b006195754018deec964e6e190b7e8c9ac8f
148 lines
5.7 KiB
C++
148 lines
5.7 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
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// http://code.google.com/p/ceres-solver/
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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: keir@google.com (Keir Mierle)
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#include <vector>
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#include "ceres/block_evaluate_preparer.h"
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#include "ceres/block_jacobian_writer.h"
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#include "ceres/compressed_row_jacobian_writer.h"
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#include "ceres/compressed_row_sparse_matrix.h"
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#include "ceres/crs_matrix.h"
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#include "ceres/dense_jacobian_writer.h"
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#include "ceres/evaluator.h"
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#include "ceres/internal/port.h"
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#include "ceres/program_evaluator.h"
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#include "ceres/scratch_evaluate_preparer.h"
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#include "glog/logging.h"
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namespace ceres {
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namespace internal {
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Evaluator::~Evaluator() {}
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Evaluator* Evaluator::Create(const Evaluator::Options& options,
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Program* program,
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string* error) {
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switch (options.linear_solver_type) {
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case DENSE_QR:
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case DENSE_NORMAL_CHOLESKY:
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return new ProgramEvaluator<ScratchEvaluatePreparer,
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DenseJacobianWriter>(options,
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program);
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case DENSE_SCHUR:
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case SPARSE_SCHUR:
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case ITERATIVE_SCHUR:
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case CGNR:
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return new ProgramEvaluator<BlockEvaluatePreparer,
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BlockJacobianWriter>(options,
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program);
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case SPARSE_NORMAL_CHOLESKY:
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return new ProgramEvaluator<ScratchEvaluatePreparer,
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CompressedRowJacobianWriter>(options,
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program);
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default:
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*error = "Invalid Linear Solver Type. Unable to create evaluator.";
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return NULL;
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}
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}
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bool Evaluator::Evaluate(Program* program,
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int num_threads,
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double* cost,
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vector<double>* residuals,
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vector<double>* gradient,
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CRSMatrix* output_jacobian) {
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CHECK_GE(num_threads, 1)
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<< "This is a Ceres bug; please contact the developers!";
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CHECK_NOTNULL(cost);
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// Setup the Parameter indices and offsets before an evaluator can
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// be constructed and used.
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program->SetParameterOffsetsAndIndex();
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Evaluator::Options evaluator_options;
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evaluator_options.linear_solver_type = SPARSE_NORMAL_CHOLESKY;
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evaluator_options.num_threads = num_threads;
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string error;
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scoped_ptr<Evaluator> evaluator(
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Evaluator::Create(evaluator_options, program, &error));
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if (evaluator.get() == NULL) {
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LOG(ERROR) << "Unable to create an Evaluator object. "
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<< "Error: " << error
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<< "This is a Ceres bug; please contact the developers!";
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return false;
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}
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if (residuals !=NULL) {
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residuals->resize(evaluator->NumResiduals());
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}
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if (gradient != NULL) {
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gradient->resize(evaluator->NumEffectiveParameters());
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}
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scoped_ptr<CompressedRowSparseMatrix> jacobian;
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if (output_jacobian != NULL) {
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jacobian.reset(
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down_cast<CompressedRowSparseMatrix*>(evaluator->CreateJacobian()));
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}
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// Point the state pointers to the user state pointers. This is
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// needed so that we can extract a parameter vector which is then
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// passed to Evaluator::Evaluate.
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program->SetParameterBlockStatePtrsToUserStatePtrs();
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// Copy the value of the parameter blocks into a vector, since the
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// Evaluate::Evaluate method needs its input as such. The previous
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// call to SetParameterBlockStatePtrsToUserStatePtrs ensures that
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// these values are the ones corresponding to the actual state of
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// the parameter blocks, rather than the temporary state pointer
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// used for evaluation.
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Vector parameters(program->NumParameters());
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program->ParameterBlocksToStateVector(parameters.data());
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if (!evaluator->Evaluate(parameters.data(),
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cost,
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residuals != NULL ? &(*residuals)[0] : NULL,
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gradient != NULL ? &(*gradient)[0] : NULL,
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jacobian.get())) {
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return false;
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}
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if (output_jacobian != NULL) {
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jacobian->ToCRSMatrix(output_jacobian);
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}
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return true;
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}
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} // namespace internal
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} // namespace ceres
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