mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-29 16:40:38 +08:00
Sameer Agarwal
b44cfdef25
Up till now ITERATIVE_SCHUR evaluates matrix-vector products between the Schur complement and a vector implicitly by exploiting the algebraic expression for the Schur complement. This cost of this evaluation scales with the number of non-zeros in the Jacobian. For small to medium sized problems there is a sweet spot where computing the Schur complement is cheap enough that it is much more efficient to explicitly compute it and use it for evaluating the matrix-vector products. This changes implements support for an explicit Schur complement in ITERATIVE_SCHUR in combination with the SCHUR_JACOBI preconditioner. API wise a new bool Solver::Options::use_explicit_schur_complement has been added. The implementation extends the SparseSchurComplementSolver to use Conjugate Gradients. Example speedup: use_explicit_schur_complement = false Time (in seconds): Preprocessor 0.585 Residual evaluation 0.319 Jacobian evaluation 1.590 Linear solver 25.685 Minimizer 27.990 Postprocessor 0.010 Total 28.585 use_explicit_schur_complement = true Time (in seconds): Preprocessor 0.638 Residual evaluation 0.318 Jacobian evaluation 1.507 Linear solver 5.930 Minimizer 8.144 Postprocessor 0.010 Total 8.791 Which indicates an end-to-end speedup of more than 3x, with the linear solver being sped up by > 4x. The idea to explore this optimization was inspired by the recent paper: Mining structure fragments for smart bundle adjustment L. Carlone, P. Alcantarilla, H. Chiu, K. Zsolt, F. Dellaert British Machine Vision Conference, 2014 which uses a more complicated algorithm to compute parts of the Schur complement to speed up the matrix-vector product. Change-Id: I95324af0ab351faa1600f5204039a1d2a64ae61d
120 lines
4.0 KiB
C++
120 lines
4.0 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: sameeragarwal@google.com (Sameer Agarwal)
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#include "ceres/linear_solver.h"
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#include "ceres/cgnr_solver.h"
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#include "ceres/dense_normal_cholesky_solver.h"
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#include "ceres/dense_qr_solver.h"
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#include "ceres/iterative_schur_complement_solver.h"
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#include "ceres/schur_complement_solver.h"
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#include "ceres/sparse_normal_cholesky_solver.h"
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#include "ceres/types.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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LinearSolver::~LinearSolver() {
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}
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LinearSolverType LinearSolver::LinearSolverForZeroEBlocks(
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LinearSolverType linear_solver_type) {
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if (!IsSchurType(linear_solver_type)) {
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return linear_solver_type;
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}
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if (linear_solver_type == SPARSE_SCHUR) {
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return SPARSE_NORMAL_CHOLESKY;
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}
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if (linear_solver_type == DENSE_SCHUR) {
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// TODO(sameeragarwal): This is probably not a great choice.
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// Ideally, we should have a DENSE_NORMAL_CHOLESKY, that can take
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// a BlockSparseMatrix as input.
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return DENSE_QR;
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}
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if (linear_solver_type == ITERATIVE_SCHUR) {
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return CGNR;
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}
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return linear_solver_type;
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}
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LinearSolver* LinearSolver::Create(const LinearSolver::Options& options) {
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switch (options.type) {
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case CGNR:
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return new CgnrSolver(options);
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case SPARSE_NORMAL_CHOLESKY:
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#if defined(CERES_NO_SUITESPARSE) && \
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defined(CERES_NO_CXSPARSE) && \
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!defined(CERES_USE_EIGEN_SPARSE)
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return NULL;
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#else
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return new SparseNormalCholeskySolver(options);
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#endif
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case SPARSE_SCHUR:
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#if defined(CERES_NO_SUITESPARSE) && \
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defined(CERES_NO_CXSPARSE) && \
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!defined(CERES_USE_EIGEN_SPARSE)
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return NULL;
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#else
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return new SparseSchurComplementSolver(options);
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#endif
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case DENSE_SCHUR:
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return new DenseSchurComplementSolver(options);
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case ITERATIVE_SCHUR:
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if (options.use_explicit_schur_complement) {
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return new SparseSchurComplementSolver(options);
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} else {
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return new IterativeSchurComplementSolver(options);
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}
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case DENSE_QR:
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return new DenseQRSolver(options);
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case DENSE_NORMAL_CHOLESKY:
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return new DenseNormalCholeskySolver(options);
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default:
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LOG(FATAL) << "Unknown linear solver type :"
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<< options.type;
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return NULL; // MSVC doesn't understand that LOG(FATAL) never returns.
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}
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}
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} // namespace internal
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} // namespace ceres
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