| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2015 Google Inc. All rights reserved. |
| // http://ceres-solver.org/ |
| // |
| // Redistribution and use in source and binary forms, with or without |
| // modification, are permitted provided that the following conditions are met: |
| // |
| // * Redistributions of source code must retain the above copyright notice, |
| // this list of conditions and the following disclaimer. |
| // * Redistributions in binary form must reproduce the above copyright notice, |
| // this list of conditions and the following disclaimer in the documentation |
| // and/or other materials provided with the distribution. |
| // * Neither the name of Google Inc. nor the names of its contributors may be |
| // used to endorse or promote products derived from this software without |
| // specific prior written permission. |
| // |
| // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" |
| // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE |
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| // POSSIBILITY OF SUCH DAMAGE. |
| // |
| // Author: sameeragarwal@google.com (Sameer Agarwal) |
| |
| #ifndef CERES_INTERNAL_PRECONDITIONER_H_ |
| #define CERES_INTERNAL_PRECONDITIONER_H_ |
| |
| #include <vector> |
| |
| #include "ceres/casts.h" |
| #include "ceres/compressed_row_sparse_matrix.h" |
| #include "ceres/context_impl.h" |
| #include "ceres/internal/disable_warnings.h" |
| #include "ceres/internal/export.h" |
| #include "ceres/linear_operator.h" |
| #include "ceres/linear_solver.h" |
| #include "ceres/sparse_matrix.h" |
| #include "ceres/types.h" |
| |
| namespace ceres::internal { |
| |
| class BlockSparseMatrix; |
| class SparseMatrix; |
| |
| class CERES_NO_EXPORT Preconditioner : public LinearOperator { |
| public: |
| struct Options { |
| PreconditionerType type = JACOBI; |
| VisibilityClusteringType visibility_clustering_type = CANONICAL_VIEWS; |
| SparseLinearAlgebraLibraryType sparse_linear_algebra_library_type = |
| SUITE_SPARSE; |
| OrderingType ordering_type = OrderingType::NATURAL; |
| |
| // When using the subset preconditioner, all row blocks starting |
| // from this row block are used to construct the preconditioner. |
| // |
| // i.e., the Jacobian matrix A is horizontally partitioned as |
| // |
| // A = [P] |
| // [Q] |
| // |
| // where P has subset_preconditioner_start_row_block row blocks, |
| // and the preconditioner is the inverse of the matrix Q'Q. |
| int subset_preconditioner_start_row_block = -1; |
| |
| // If possible, how many threads the preconditioner can use. |
| int num_threads = 1; |
| |
| // Hints about the order in which the parameter blocks should be |
| // eliminated by the linear solver. |
| // |
| // For example if elimination_groups is a vector of size k, then |
| // the linear solver is informed that it should eliminate the |
| // parameter blocks 0 ... elimination_groups[0] - 1 first, and |
| // then elimination_groups[0] ... elimination_groups[1] - 1 and so |
| // on. Within each elimination group, the linear solver is free to |
| // choose how the parameter blocks are ordered. Different linear |
| // solvers have differing requirements on elimination_groups. |
| // |
| // The most common use is for Schur type solvers, where there |
| // should be at least two elimination groups and the first |
| // elimination group must form an independent set in the normal |
| // equations. The first elimination group corresponds to the |
| // num_eliminate_blocks in the Schur type solvers. |
| std::vector<int> elimination_groups; |
| |
| // If the block sizes in a BlockSparseMatrix are fixed, then in |
| // some cases the Schur complement based solvers can detect and |
| // specialize on them. |
| // |
| // It is expected that these parameters are set programmatically |
| // rather than manually. |
| // |
| // Please see schur_complement_solver.h and schur_eliminator.h for |
| // more details. |
| int row_block_size = Eigen::Dynamic; |
| int e_block_size = Eigen::Dynamic; |
| int f_block_size = Eigen::Dynamic; |
| |
| ContextImpl* context = nullptr; |
| }; |
| |
| // If the optimization problem is such that there are no remaining |
| // e-blocks, ITERATIVE_SCHUR with a Schur type preconditioner cannot |
| // be used. This function returns JACOBI if a preconditioner for |
| // ITERATIVE_SCHUR is used. The input preconditioner_type is |
| // returned otherwise. |
| static PreconditionerType PreconditionerForZeroEBlocks( |
| PreconditionerType preconditioner_type); |
| |
| ~Preconditioner() override; |
| |
| // Update the numerical value of the preconditioner for the linear |
| // system: |
| // |
| // | A | x = |b| |
| // |diag(D)| |0| |
| // |
| // for some vector b. It is important that the matrix A have the |
| // same block structure as the one used to construct this object. |
| // |
| // D can be nullptr, in which case its interpreted as a diagonal matrix |
| // of size zero. |
| virtual bool Update(const LinearOperator& A, const double* D) = 0; |
| |
| // LinearOperator interface. Since the operator is symmetric, |
| // LeftMultiplyAndAccumulate and num_cols are just calls to |
| // RightMultiplyAndAccumulate and num_rows respectively. Update() must be |
| // called before RightMultiplyAndAccumulate can be called. |
| void RightMultiplyAndAccumulate(const double* x, |
| double* y) const override = 0; |
| void LeftMultiplyAndAccumulate(const double* x, double* y) const override { |
| return RightMultiplyAndAccumulate(x, y); |
| } |
| |
| int num_rows() const override = 0; |
| int num_cols() const override { return num_rows(); } |
| }; |
| |
| class CERES_NO_EXPORT IdentityPreconditioner : public Preconditioner { |
| public: |
| IdentityPreconditioner(int num_rows) : num_rows_(num_rows) {} |
| |
| bool Update(const LinearOperator& A, const double* D) final { return true; } |
| |
| void RightMultiplyAndAccumulate(const double* x, double* y) const final { |
| VectorRef(y, num_rows_) += ConstVectorRef(x, num_rows_); |
| } |
| |
| int num_rows() const final { return num_rows_; } |
| |
| private: |
| int num_rows_ = -1; |
| }; |
| |
| // This templated subclass of Preconditioner serves as a base class for |
| // other preconditioners that depend on the particular matrix layout of |
| // the underlying linear operator. |
| template <typename MatrixType> |
| class CERES_NO_EXPORT TypedPreconditioner : public Preconditioner { |
| public: |
| bool Update(const LinearOperator& A, const double* D) final { |
| return UpdateImpl(*down_cast<const MatrixType*>(&A), D); |
| } |
| |
| private: |
| virtual bool UpdateImpl(const MatrixType& A, const double* D) = 0; |
| }; |
| |
| // Preconditioners that depend on access to the low level structure |
| // of a SparseMatrix. |
| // clang-format off |
| using SparseMatrixPreconditioner = TypedPreconditioner<SparseMatrix>; |
| using BlockSparseMatrixPreconditioner = TypedPreconditioner<BlockSparseMatrix>; |
| using CompressedRowSparseMatrixPreconditioner = TypedPreconditioner<CompressedRowSparseMatrix>; |
| // clang-format on |
| |
| // Wrap a SparseMatrix object as a preconditioner. |
| class CERES_NO_EXPORT SparseMatrixPreconditionerWrapper final |
| : public SparseMatrixPreconditioner { |
| public: |
| // Wrapper does NOT take ownership of the matrix pointer. |
| explicit SparseMatrixPreconditionerWrapper(const SparseMatrix* matrix); |
| ~SparseMatrixPreconditionerWrapper() override; |
| |
| // Preconditioner interface |
| void RightMultiplyAndAccumulate(const double* x, double* y) const override; |
| int num_rows() const override; |
| |
| private: |
| bool UpdateImpl(const SparseMatrix& A, const double* D) override; |
| const SparseMatrix* matrix_; |
| }; |
| |
| } // namespace ceres::internal |
| |
| #include "ceres/internal/reenable_warnings.h" |
| |
| #endif // CERES_INTERNAL_PRECONDITIONER_H_ |