mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-29 16:40:38 +08:00
Sameer Agarwal
f973e107d2
1. Add Solver::Options::use_mixed_precision_solves, and Solver::Options::max_num_refinement_iterations. 2. Make SparseCholesky::Create return a unique_ptr. 3. SparseCholesky::Create now takes LinearSolver::Options as an argument. 4. IterativeRefiner's constructor does not require num_cols as an argument. 5. SparseNormalCholeskySolver now uses a separate rhs vector. This basic implementation results in a 10% reduction in solver time and 30% reduction in linear solver memory usage. Change-Id: I6830f32cae2febf082d2733262eb2c9f0482b0ea
115 lines
4.1 KiB
C++
115 lines
4.1 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/sparse_normal_cholesky_solver.h"
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#include <algorithm>
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#include <cstring>
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#include <ctime>
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#include <memory>
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#include "ceres/block_sparse_matrix.h"
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#include "ceres/inner_product_computer.h"
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#include "ceres/internal/eigen.h"
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#include "ceres/iterative_refiner.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/triplet_sparse_matrix.h"
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#include "ceres/types.h"
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#include "ceres/wall_time.h"
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namespace ceres {
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namespace internal {
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SparseNormalCholeskySolver::SparseNormalCholeskySolver(
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const LinearSolver::Options& options)
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: options_(options) {
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sparse_cholesky_ = SparseCholesky::Create(options);
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}
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SparseNormalCholeskySolver::~SparseNormalCholeskySolver() {}
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LinearSolver::Summary SparseNormalCholeskySolver::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("SparseNormalCholeskySolver::Solve");
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LinearSolver::Summary summary;
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summary.num_iterations = 1;
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summary.termination_type = LINEAR_SOLVER_SUCCESS;
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summary.message = "Success.";
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const int num_cols = A->num_cols();
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VectorRef xref(x, num_cols);
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xref.setZero();
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rhs_.resize(num_cols);
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rhs_.setZero();
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A->LeftMultiply(b, rhs_.data());
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event_logger.AddEvent("Compute RHS");
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if (per_solve_options.D != NULL) {
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// Temporarily append a diagonal block to the A matrix, but undo
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// it before returning the matrix to the user.
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std::unique_ptr<BlockSparseMatrix> regularizer;
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regularizer.reset(BlockSparseMatrix::CreateDiagonalMatrix(
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per_solve_options.D, A->block_structure()->cols));
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event_logger.AddEvent("Diagonal");
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A->AppendRows(*regularizer);
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event_logger.AddEvent("Append");
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}
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event_logger.AddEvent("Append Rows");
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if (inner_product_computer_.get() == NULL) {
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inner_product_computer_.reset(
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InnerProductComputer::Create(*A, sparse_cholesky_->StorageType()));
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event_logger.AddEvent("InnerProductComputer::Create");
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}
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inner_product_computer_->Compute();
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event_logger.AddEvent("InnerProductComputer::Compute");
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if (per_solve_options.D != NULL) {
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A->DeleteRowBlocks(A->block_structure()->cols.size());
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}
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summary.termination_type = sparse_cholesky_->FactorAndSolve(
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inner_product_computer_->mutable_result(),
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rhs_.data(),
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x,
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&summary.message);
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event_logger.AddEvent("SparseCholesky::FactorAndSolve");
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return summary;
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}
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} // namespace internal
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} // namespace ceres
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