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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2015 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
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//
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// Author: sameeragarwal@google.com (Sameer Agarwal)
//
// An iterative solver for solving the Schur complement/reduced camera
// linear system that arise in SfM problems.
#ifndef CERES_INTERNAL_IMPLICIT_SCHUR_COMPLEMENT_H_
#define CERES_INTERNAL_IMPLICIT_SCHUR_COMPLEMENT_H_
#include <memory>
#include "ceres/internal/disable_warnings.h"
#include "ceres/internal/eigen.h"
#include "ceres/internal/export.h"
#include "ceres/linear_operator.h"
#include "ceres/linear_solver.h"
#include "ceres/partitioned_matrix_view.h"
#include "ceres/types.h"
namespace ceres::internal {
class BlockSparseMatrix;
// This class implements various linear algebraic operations related
// to the Schur complement without explicitly forming it.
//
//
// Given a reactangular linear system Ax = b, where
//
// A = [E F]
//
// The normal equations are given by
//
// A'Ax = A'b
//
// |E'E E'F||y| = |E'b|
// |F'E F'F||z| |F'b|
//
// and the Schur complement system is given by
//
// [F'F - F'E (E'E)^-1 E'F] z = F'b - F'E (E'E)^-1 E'b
//
// Now if we wish to solve Ax = b in the least squares sense, one way
// is to form this Schur complement system and solve it using
// Preconditioned Conjugate Gradients.
//
// The key operation in a conjugate gradient solver is the evaluation of the
// matrix vector product with the Schur complement
//
// S = F'F - F'E (E'E)^-1 E'F
//
// It is straightforward to see that matrix vector products with S can
// be evaluated without storing S in memory. Instead, given (E'E)^-1
// (which for our purposes is an easily inverted block diagonal
// matrix), it can be done in terms of matrix vector products with E,
// F and (E'E)^-1. This class implements this functionality and other
// auxiliary bits needed to implement a CG solver on the Schur
// complement using the PartitionedMatrixView object.
//
// THREAD SAFETY: This class is not thread safe. In particular, the
// RightMultiplyAndAccumulate (and the LeftMultiplyAndAccumulate) methods are
// not thread safe as they depend on mutable arrays used for the temporaries
// needed to compute the product y += Sx;
class CERES_NO_EXPORT ImplicitSchurComplement final : public LinearOperator {
public:
// num_eliminate_blocks is the number of E blocks in the matrix
// A.
//
// preconditioner indicates whether the inverse of the matrix F'F
// should be computed or not as a preconditioner for the Schur
// Complement.
//
// TODO(sameeragarwal): Get rid of the two bools below and replace
// them with enums.
explicit ImplicitSchurComplement(const LinearSolver::Options& options);
// Initialize the Schur complement for a linear least squares
// problem of the form
//
// |A | x = |b|
// |diag(D)| |0|
//
// If D is null, then it is treated as a zero dimensional matrix. It
// is important that the matrix A have a BlockStructure object
// associated with it and has a block structure that is compatible
// with the SchurComplement solver.
void Init(const BlockSparseMatrix& A, const double* D, const double* b);
// y += Sx, where S is the Schur complement.
void RightMultiplyAndAccumulate(const double* x, double* y) const final;
// The Schur complement is a symmetric positive definite matrix,
// thus the left and right multiply operators are the same.
void LeftMultiplyAndAccumulate(const double* x, double* y) const final {
RightMultiplyAndAccumulate(x, y);
}
// Following is useful for approximation of S^-1 via power series expansion.
// Z = (F'F)^-1 F'E (E'E)^-1 E'F
// y += Zx
void InversePowerSeriesOperatorRightMultiplyAccumulate(const double* x,
double* y) const;
// y = (E'E)^-1 (E'b - E'F x). Given an estimate of the solution to
// the Schur complement system, this method computes the value of
// the e_block variables that were eliminated to form the Schur
// complement.
void BackSubstitute(const double* x, double* y);
int num_rows() const final { return A_->num_cols_f(); }
int num_cols() const final { return A_->num_cols_f(); }
const Vector& rhs() const { return rhs_; }
const BlockSparseMatrix* block_diagonal_EtE_inverse() const {
return block_diagonal_EtE_inverse_.get();
}
const BlockSparseMatrix* block_diagonal_FtF_inverse() const {
CHECK(compute_ftf_inverse_);
return block_diagonal_FtF_inverse_.get();
}
private:
void AddDiagonalAndInvert(const double* D, BlockSparseMatrix* matrix);
void UpdateRhs();
const LinearSolver::Options& options_;
bool compute_ftf_inverse_ = false;
std::unique_ptr<PartitionedMatrixViewBase> A_;
const double* D_ = nullptr;
const double* b_ = nullptr;
std::unique_ptr<BlockSparseMatrix> block_diagonal_EtE_inverse_;
std::unique_ptr<BlockSparseMatrix> block_diagonal_FtF_inverse_;
Vector rhs_;
// Temporary storage vectors used to implement RightMultiplyAndAccumulate.
mutable Vector tmp_rows_;
mutable Vector tmp_e_cols_;
mutable Vector tmp_e_cols_2_;
mutable Vector tmp_f_cols_;
};
} // namespace ceres::internal
#include "ceres/internal/reenable_warnings.h"
#endif // CERES_INTERNAL_IMPLICIT_SCHUR_COMPLEMENT_H_