mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-29 16:40:38 +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
117 lines
4.2 KiB
C++
117 lines
4.2 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2017 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/subset_preconditioner.h"
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#include <memory>
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#include <string>
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#include <utility>
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#include "ceres/compressed_row_sparse_matrix.h"
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#include "ceres/inner_product_computer.h"
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#include "ceres/linear_solver.h"
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#include "ceres/sparse_cholesky.h"
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#include "ceres/types.h"
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namespace ceres::internal {
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SubsetPreconditioner::SubsetPreconditioner(Preconditioner::Options options,
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const BlockSparseMatrix& A)
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: options_(std::move(options)), num_cols_(A.num_cols()) {
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CHECK_GE(options_.subset_preconditioner_start_row_block, 0)
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<< "Congratulations, you found a bug in Ceres. Please report it.";
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LinearSolver::Options sparse_cholesky_options;
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sparse_cholesky_options.sparse_linear_algebra_library_type =
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options_.sparse_linear_algebra_library_type;
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sparse_cholesky_options.ordering_type = options_.ordering_type;
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sparse_cholesky_ = SparseCholesky::Create(sparse_cholesky_options);
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}
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SubsetPreconditioner::~SubsetPreconditioner() = default;
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void SubsetPreconditioner::RightMultiply(const double* x, double* y) const {
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CHECK(x != nullptr);
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CHECK(y != nullptr);
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std::string message;
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sparse_cholesky_->Solve(x, y, &message);
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}
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bool SubsetPreconditioner::UpdateImpl(const BlockSparseMatrix& A,
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const double* D) {
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auto* m = const_cast<BlockSparseMatrix*>(&A);
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const CompressedRowBlockStructure* bs = m->block_structure();
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// A = [P]
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// [Q]
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// Now add D to A if needed.
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if (D != nullptr) {
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// A = [P]
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// [Q]
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// [D]
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std::unique_ptr<BlockSparseMatrix> regularizer(
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BlockSparseMatrix::CreateDiagonalMatrix(D, bs->cols));
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m->AppendRows(*regularizer);
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}
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if (inner_product_computer_ == nullptr) {
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inner_product_computer_ = InnerProductComputer::Create(
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*m,
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options_.subset_preconditioner_start_row_block,
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bs->rows.size(),
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sparse_cholesky_->StorageType());
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}
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// Compute inner_product = [Q'*Q + D'*D]
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inner_product_computer_->Compute();
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// Unappend D if needed.
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if (D != nullptr) {
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// A = [P]
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// [Q]
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m->DeleteRowBlocks(bs->cols.size());
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}
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std::string message;
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// Compute L. s.t., LL' = Q'*Q + D'*D
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const LinearSolverTerminationType termination_type =
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sparse_cholesky_->Factorize(inner_product_computer_->mutable_result(),
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&message);
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if (termination_type != LinearSolverTerminationType::SUCCESS) {
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LOG(ERROR) << "Preconditioner factorization failed: " << message;
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return false;
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}
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return true;
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}
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} // namespace ceres::internal
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