| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2023 Google Inc. All rights reserved. |
| // http://ceres-solver.org/ |
| // |
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| // modification, are permitted provided that the following conditions are met: |
| // |
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| // this list of conditions and the following disclaimer in the documentation |
| // and/or other materials provided with the distribution. |
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| // specific prior written permission. |
| // |
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| // |
| // Author: sameeragarwal@google.com (Sameer Agarwal) |
| |
| #ifndef CERES_INTERNAL_ITERATIVE_REFINER_H_ |
| #define CERES_INTERNAL_ITERATIVE_REFINER_H_ |
| |
| // This include must come before any #ifndef check on Ceres compile options. |
| // clang-format off |
| #include "ceres/internal/config.h" |
| // clang-format on |
| |
| #include "ceres/internal/eigen.h" |
| #include "ceres/internal/export.h" |
| |
| namespace ceres::internal { |
| |
| class DenseCholesky; |
| class SparseCholesky; |
| class SparseMatrix; |
| |
| // Iterative refinement |
| // (https://en.wikipedia.org/wiki/Iterative_refinement) is the process |
| // of improving the solution to a linear system, by using the |
| // following iteration. |
| // |
| // r_i = b - Ax_i |
| // Ad_i = r_i |
| // x_{i+1} = x_i + d_i |
| // |
| // IterativeRefiner implements this process for Symmetric Positive |
| // Definite linear systems. |
| // |
| // The above iterative loop is run until max_num_iterations is reached. |
| class CERES_NO_EXPORT SparseIterativeRefiner { |
| public: |
| // max_num_iterations is the number of refinement iterations to |
| // perform. |
| explicit SparseIterativeRefiner(int max_num_iterations); |
| |
| // Needed for mocking. |
| virtual ~SparseIterativeRefiner(); |
| |
| // Given an initial estimate of the solution of lhs * x = rhs, use |
| // max_num_iterations rounds of iterative refinement to improve it. |
| // |
| // cholesky is assumed to contain an already computed factorization (or |
| // an approximation thereof) of lhs. |
| // |
| // solution is expected to contain a approximation to the solution |
| // to lhs * x = rhs. It can be zero. |
| // |
| // This method is virtual to facilitate mocking. |
| virtual void Refine(const SparseMatrix& lhs, |
| const double* rhs, |
| SparseCholesky* cholesky, |
| double* solution); |
| |
| private: |
| void Allocate(int num_cols); |
| |
| int max_num_iterations_; |
| Vector residual_; |
| Vector correction_; |
| Vector lhs_x_solution_; |
| }; |
| |
| class CERES_NO_EXPORT DenseIterativeRefiner { |
| public: |
| // max_num_iterations is the number of refinement iterations to |
| // perform. |
| explicit DenseIterativeRefiner(int max_num_iterations); |
| |
| // Needed for mocking. |
| virtual ~DenseIterativeRefiner(); |
| |
| // Given an initial estimate of the solution of lhs * x = rhs, use |
| // max_num_iterations rounds of iterative refinement to improve it. |
| // |
| // cholesky is assumed to contain an already computed factorization (or |
| // an approximation thereof) of lhs. |
| // |
| // solution is expected to contain a approximation to the solution |
| // to lhs * x = rhs. It can be zero. |
| // |
| // This method is virtual to facilitate mocking. |
| virtual void Refine(int num_cols, |
| const double* lhs, |
| const double* rhs, |
| DenseCholesky* cholesky, |
| double* solution); |
| |
| private: |
| void Allocate(int num_cols); |
| |
| int max_num_iterations_; |
| Vector residual_; |
| Vector correction_; |
| }; |
| |
| } // namespace ceres::internal |
| |
| #endif // CERES_INTERNAL_ITERATIVE_REFINER_H_ |