mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-29 16:40:38 +08:00
Sameer Agarwal
bdda32bb16
A simple class that composes SparseCholesky with IterativeRefiner. Change-Id: I4a67b8ca33a604aaa7b6a4bf511dad9501815f5b
115 lines
4.4 KiB
C++
115 lines
4.4 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2018 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 <string>
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#include "ceres/iterative_refiner.h"
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#include "Eigen/Core"
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#include "ceres/sparse_cholesky.h"
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#include "ceres/sparse_matrix.h"
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namespace ceres {
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namespace internal {
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IterativeRefiner::IterativeRefiner(const int num_cols,
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const int max_num_iterations)
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: num_cols_(num_cols),
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max_num_iterations_(max_num_iterations),
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residual_(num_cols),
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correction_(num_cols),
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lhs_x_solution_(num_cols) {}
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IterativeRefiner::~IterativeRefiner() {}
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IterativeRefiner::Summary IterativeRefiner::Refine(
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const SparseMatrix& lhs,
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const double* rhs_ptr,
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SparseCholesky* sparse_cholesky,
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double* solution_ptr) {
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Summary summary;
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ConstVectorRef rhs(rhs_ptr, num_cols_);
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VectorRef solution(solution_ptr, num_cols_);
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summary.lhs_max_norm = ConstVectorRef(lhs.values(), lhs.num_nonzeros())
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.lpNorm<Eigen::Infinity>();
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summary.rhs_max_norm = rhs.lpNorm<Eigen::Infinity>();
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summary.solution_max_norm = solution.lpNorm<Eigen::Infinity>();
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// residual = rhs - lhs * solution
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lhs_x_solution_.setZero();
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lhs.RightMultiply(solution_ptr, lhs_x_solution_.data());
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residual_ = rhs - lhs_x_solution_;
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summary.residual_max_norm = residual_.lpNorm<Eigen::Infinity>();
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for (summary.num_iterations = 0;
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summary.num_iterations < max_num_iterations_;
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++summary.num_iterations) {
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// Check the current solution for convergence.
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const double kTolerance = 5e-15; // From Hogg & Scott.
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// residual_tolerance = (|A| |x| + |b|) * kTolerance;
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const double residual_tolerance =
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(summary.lhs_max_norm * summary.solution_max_norm +
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summary.rhs_max_norm) *
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kTolerance;
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VLOG(3) << "Refinement:"
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<< " iter: " << summary.num_iterations
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<< " |A|: " << summary.lhs_max_norm
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<< " |b|: " << summary.rhs_max_norm
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<< " |x|: " << summary.solution_max_norm
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<< " |b - Ax|: " << summary.residual_max_norm
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<< " tol: " << residual_tolerance;
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// |b - Ax| < (|A| |x| + |b|) * kTolerance;
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if (summary.residual_max_norm < residual_tolerance) {
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summary.converged = true;
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break;
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}
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// Solve for lhs * correction = residual
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correction_.setZero();
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std::string ignored_message;
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sparse_cholesky->Solve(
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residual_.data(), correction_.data(), &ignored_message);
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solution += correction_;
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summary.solution_max_norm = solution.lpNorm<Eigen::Infinity>();
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// residual = rhs - lhs * solution
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lhs_x_solution_.setZero();
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lhs.RightMultiply(solution_ptr, lhs_x_solution_.data());
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residual_ = rhs - lhs_x_solution_;
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summary.residual_max_norm = residual_.lpNorm<Eigen::Infinity>();
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}
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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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