mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-29 08:34:37 +08:00
Sameer Agarwal
41c5fb1e80
1. Generalize SuiteSparse::AnalyzeCholesky and SuiteSparse::BlockAnalyzeCholesky from just doing AMD to taking OrderingType as an argument and using that to determine whether AMD & Nested Dissection algorithms are used for computing the fill-reducing ordering or a natural ordering when computing the symbolic factorization. 2. Remove AnalyzeCholeskyWithNaturalOrdering. 3. Replace and generalize SuiteSparse::BlockAMDOrdering with SuiteSparse::BlockOrdering which also takes OrderingType as an argument. Same for SuiteSparse::ApproximateMinimumDegreeOrdering and SuiteSparse::NestedDissectionOrdering by SuiteSparse::Ordering. 4. Remove LinearSolver::Options::use_postordering and replace it with LinearSolver::Options::ordering_type. 5. Replace Preconditioner::Options::use_postordering and replace it with Preconditioner::Options::ordering_type. 6. Add NESDIS to OrderingType. With the above changes, the linear solvers can now use Nested Dissection once this information is piped through the nonlinear solver. Change-Id: Ib8e93fbf34ae2981bf2ac54dcda9e25c7c213790
111 lines
4.3 KiB
C++
111 lines
4.3 KiB
C++
// 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: keir@google.com (Keir Mierle)
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#include "ceres/cgnr_solver.h"
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#include <memory>
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#include <utility>
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#include "ceres/block_jacobi_preconditioner.h"
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#include "ceres/cgnr_linear_operator.h"
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#include "ceres/conjugate_gradients_solver.h"
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#include "ceres/internal/eigen.h"
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#include "ceres/linear_solver.h"
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#include "ceres/subset_preconditioner.h"
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#include "ceres/wall_time.h"
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#include "glog/logging.h"
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namespace ceres::internal {
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CgnrSolver::CgnrSolver(LinearSolver::Options options)
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: options_(std::move(options)) {
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if (options_.preconditioner_type != JACOBI &&
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options_.preconditioner_type != IDENTITY &&
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options_.preconditioner_type != SUBSET) {
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LOG(FATAL)
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<< "Preconditioner = "
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<< PreconditionerTypeToString(options_.preconditioner_type) << ". "
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<< "Congratulations, you found a bug in Ceres. Please report it.";
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}
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}
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CgnrSolver::~CgnrSolver() = default;
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LinearSolver::Summary CgnrSolver::SolveImpl(
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BlockSparseMatrix* A,
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const double* b,
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const LinearSolver::PerSolveOptions& per_solve_options,
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double* x) {
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EventLogger event_logger("CgnrSolver::Solve");
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// Form z = Atb.
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Vector z(A->num_cols());
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z.setZero();
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A->LeftMultiply(b, z.data());
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if (!preconditioner_) {
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if (options_.preconditioner_type == JACOBI) {
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preconditioner_ = std::make_unique<BlockJacobiPreconditioner>(*A);
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} else if (options_.preconditioner_type == SUBSET) {
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Preconditioner::Options preconditioner_options;
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preconditioner_options.type = SUBSET;
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preconditioner_options.subset_preconditioner_start_row_block =
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options_.subset_preconditioner_start_row_block;
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preconditioner_options.sparse_linear_algebra_library_type =
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options_.sparse_linear_algebra_library_type;
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preconditioner_options.ordering_type = options_.ordering_type;
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preconditioner_options.num_threads = options_.num_threads;
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preconditioner_options.context = options_.context;
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preconditioner_ =
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std::make_unique<SubsetPreconditioner>(preconditioner_options, *A);
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}
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}
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if (preconditioner_) {
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preconditioner_->Update(*A, per_solve_options.D);
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}
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LinearSolver::PerSolveOptions cg_per_solve_options = per_solve_options;
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cg_per_solve_options.preconditioner = preconditioner_.get();
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// Solve (AtA + DtD)x = z (= Atb).
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VectorRef(x, A->num_cols()).setZero();
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CgnrLinearOperator lhs(*A, per_solve_options.D);
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event_logger.AddEvent("Setup");
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ConjugateGradientsSolver conjugate_gradient_solver(options_);
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LinearSolver::Summary summary =
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conjugate_gradient_solver.Solve(&lhs, z.data(), cg_per_solve_options, x);
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event_logger.AddEvent("Solve");
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return summary;
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}
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} // namespace ceres::internal
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