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https://github.com/ceres-solver/ceres-solver.git
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a9d8ef847f
unnecessarily complexity in the structure of linear solvers and preconditioners. This is the first step towards cleaning up the Preconditioner interface. 2. Minor tweaks and cleanups to the various linear solvers.
229 lines
8.0 KiB
C++
229 lines
8.0 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
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// http://code.google.com/p/ceres-solver/
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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 "ceres/implicit_schur_complement.h"
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#include <glog/logging.h>
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#include "Eigen/Dense"
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#include "ceres/block_sparse_matrix.h"
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#include "ceres/block_structure.h"
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#include "ceres/internal/eigen.h"
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#include "ceres/internal/scoped_ptr.h"
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#include "ceres/types.h"
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namespace ceres {
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namespace internal {
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ImplicitSchurComplement::ImplicitSchurComplement(int num_eliminate_blocks,
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bool preconditioner)
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: num_eliminate_blocks_(num_eliminate_blocks),
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preconditioner_(preconditioner),
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A_(NULL),
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D_(NULL),
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b_(NULL),
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block_diagonal_EtE_inverse_(NULL),
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block_diagonal_FtF_inverse_(NULL) {
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}
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ImplicitSchurComplement::~ImplicitSchurComplement() {
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}
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void ImplicitSchurComplement::Init(const BlockSparseMatrixBase& A,
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const double* D,
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const double* b) {
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// Since initialization is reasonably heavy, perhaps we can save on
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// constructing a new object everytime.
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if (A_ == NULL) {
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A_.reset(new PartitionedMatrixView(A, num_eliminate_blocks_));
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}
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D_ = D;
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b_ = b;
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// Initialize temporary storage and compute the block diagonals of
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// E'E and F'E.
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if (block_diagonal_EtE_inverse_ == NULL) {
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block_diagonal_EtE_inverse_.reset(A_->CreateBlockDiagonalEtE());
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if (preconditioner_) {
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block_diagonal_FtF_inverse_.reset(A_->CreateBlockDiagonalFtF());
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}
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rhs_.resize(A_->num_cols_f());
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rhs_.setZero();
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tmp_rows_.resize(A_->num_rows());
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tmp_e_cols_.resize(A_->num_cols_e());
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tmp_e_cols_2_.resize(A_->num_cols_e());
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tmp_f_cols_.resize(A_->num_cols_f());
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} else {
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A_->UpdateBlockDiagonalEtE(block_diagonal_EtE_inverse_.get());
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if (preconditioner_) {
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A_->UpdateBlockDiagonalFtF(block_diagonal_FtF_inverse_.get());
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}
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}
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// The block diagonals of the augmented linear system contain
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// contributions from the diagonal D if it is non-null. Add that to
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// the block diagonals and invert them.
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AddDiagonalAndInvert(D_, block_diagonal_EtE_inverse_.get());
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if (preconditioner_) {
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AddDiagonalAndInvert((D_ == NULL) ? NULL : D_ + A_->num_cols_e(),
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block_diagonal_FtF_inverse_.get());
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}
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// Compute the RHS of the Schur complement system.
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UpdateRhs();
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}
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// Evaluate the product
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//
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// Sx = [F'F - F'E (E'E)^-1 E'F]x
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//
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// By breaking it down into individual matrix vector products
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// involving the matrices E and F. This is implemented using a
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// PartitionedMatrixView of the input matrix A.
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void ImplicitSchurComplement::RightMultiply(const double* x, double* y) const {
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// y1 = F x
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tmp_rows_.setZero();
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A_->RightMultiplyF(x, tmp_rows_.data());
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// y2 = E' y1
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tmp_e_cols_.setZero();
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A_->LeftMultiplyE(tmp_rows_.data(), tmp_e_cols_.data());
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// y3 = -(E'E)^-1 y2
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tmp_e_cols_2_.setZero();
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block_diagonal_EtE_inverse_->RightMultiply(tmp_e_cols_.data(),
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tmp_e_cols_2_.data());
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tmp_e_cols_2_ *= -1.0;
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// y1 = y1 + E y3
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A_->RightMultiplyE(tmp_e_cols_2_.data(), tmp_rows_.data());
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// y5 = D * x
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if (D_ != NULL) {
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ConstVectorRef Dref(D_ + A_->num_cols_e(), num_cols());
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VectorRef(y, num_cols()) =
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(Dref.array().square() *
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ConstVectorRef(x, num_cols()).array()).matrix();
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} else {
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VectorRef(y, num_cols()).setZero();
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}
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// y = y5 + F' y1
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A_->LeftMultiplyF(tmp_rows_.data(), y);
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}
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// Given a block diagonal matrix and an optional array of diagonal
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// entries D, add them to the diagonal of the matrix and compute the
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// inverse of each diagonal block.
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void ImplicitSchurComplement::AddDiagonalAndInvert(
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const double* D,
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BlockSparseMatrix* block_diagonal) {
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const CompressedRowBlockStructure* block_diagonal_structure =
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block_diagonal->block_structure();
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for (int r = 0; r < block_diagonal_structure->rows.size(); ++r) {
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const int row_block_pos = block_diagonal_structure->rows[r].block.position;
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const int row_block_size = block_diagonal_structure->rows[r].block.size;
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const Cell& cell = block_diagonal_structure->rows[r].cells[0];
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MatrixRef m(block_diagonal->mutable_values() + cell.position,
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row_block_size, row_block_size);
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if (D != NULL) {
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ConstVectorRef d(D + row_block_pos, row_block_size);
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m += d.array().square().matrix().asDiagonal();
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}
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m = m
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.selfadjointView<Eigen::Upper>()
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.ldlt()
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.solve(Matrix::Identity(row_block_size, row_block_size));
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}
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}
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// Similar to RightMultiply, use the block structure of the matrix A
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// to compute y = (E'E)^-1 (E'b - E'F x).
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void ImplicitSchurComplement::BackSubstitute(const double* x, double* y) {
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const int num_cols_e = A_->num_cols_e();
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const int num_cols_f = A_->num_cols_f();
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const int num_cols = A_->num_cols();
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const int num_rows = A_->num_rows();
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// y1 = F x
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tmp_rows_.setZero();
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A_->RightMultiplyF(x, tmp_rows_.data());
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// y2 = b - y1
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tmp_rows_ = ConstVectorRef(b_, num_rows) - tmp_rows_;
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// y3 = E' y2
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tmp_e_cols_.setZero();
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A_->LeftMultiplyE(tmp_rows_.data(), tmp_e_cols_.data());
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// y = (E'E)^-1 y3
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VectorRef(y, num_cols).setZero();
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block_diagonal_EtE_inverse_->RightMultiply(tmp_e_cols_.data(), y);
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// The full solution vector y has two blocks. The first block of
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// variables corresponds to the eliminated variables, which we just
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// computed via back substitution. The second block of variables
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// corresponds to the Schur complement system, so we just copy those
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// values from the solution to the Schur complement.
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VectorRef(y + num_cols_e, num_cols_f) = ConstVectorRef(x, num_cols_f);
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}
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// Compute the RHS of the Schur complement system.
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//
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// rhs = F'b - F'E (E'E)^-1 E'b
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//
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// Like BackSubstitute, we use the block structure of A to implement
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// this using a series of matrix vector products.
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void ImplicitSchurComplement::UpdateRhs() {
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// y1 = E'b
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tmp_e_cols_.setZero();
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A_->LeftMultiplyE(b_, tmp_e_cols_.data());
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// y2 = (E'E)^-1 y1
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Vector y2 = Vector::Zero(A_->num_cols_e());
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block_diagonal_EtE_inverse_->RightMultiply(tmp_e_cols_.data(), y2.data());
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// y3 = E y2
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tmp_rows_.setZero();
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A_->RightMultiplyE(y2.data(), tmp_rows_.data());
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// y3 = b - y3
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tmp_rows_ = ConstVectorRef(b_, A_->num_rows()) - tmp_rows_;
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// rhs = F' y3
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rhs_.setZero();
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A_->LeftMultiplyF(tmp_rows_.data(), rhs_.data());
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}
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} // namespace internal
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} // namespace ceres
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