| // 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 | |
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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_ |