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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2012 Google Inc. All rights reserved.
// http://code.google.com/p/ceres-solver/
//
// 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
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Author: keir@google.com (Keir Mierle)
#include "ceres/block_jacobi_preconditioner.h"
#include "Eigen/Cholesky"
#include "ceres/block_sparse_matrix.h"
#include "ceres/block_structure.h"
#include "ceres/casts.h"
#include "ceres/integral_types.h"
#include "ceres/internal/eigen.h"
namespace ceres {
namespace internal {
BlockJacobiPreconditioner::BlockJacobiPreconditioner(const LinearOperator& A)
: num_rows_(A.num_rows()),
block_structure_(
*(down_cast<const BlockSparseMatrix*>(&A)->block_structure())) {
// Calculate the amount of storage needed.
int storage_needed = 0;
for (int c = 0; c < block_structure_.cols.size(); ++c) {
int size = block_structure_.cols[c].size;
storage_needed += size * size;
}
// Size the offsets and storage.
blocks_.resize(block_structure_.cols.size());
block_storage_.resize(storage_needed);
// Put pointers to the storage in the offsets.
double* block_cursor = &block_storage_[0];
for (int c = 0; c < block_structure_.cols.size(); ++c) {
int size = block_structure_.cols[c].size;
blocks_[c] = block_cursor;
block_cursor += size * size;
}
}
BlockJacobiPreconditioner::~BlockJacobiPreconditioner() {
}
void BlockJacobiPreconditioner::Update(const LinearOperator& matrix, const double* D) {
const BlockSparseMatrix& A = *(down_cast<const BlockSparseMatrix*>(&matrix));
const CompressedRowBlockStructure* bs = A.block_structure();
// Compute the diagonal blocks by block inner products.
std::fill(block_storage_.begin(), block_storage_.end(), 0.0);
for (int r = 0; r < bs->rows.size(); ++r) {
const int row_block_size = bs->rows[r].block.size;
const vector<Cell>& cells = bs->rows[r].cells;
const double* row_values = A.RowBlockValues(r);
for (int c = 0; c < cells.size(); ++c) {
const int col_block_size = bs->cols[cells[c].block_id].size;
ConstMatrixRef m(row_values + cells[c].position,
row_block_size,
col_block_size);
MatrixRef(blocks_[cells[c].block_id],
col_block_size,
col_block_size).noalias() += m.transpose() * m;
// TODO(keir): Figure out when the below expression is actually faster
// than doing the full rank update. The issue is that for smaller sizes,
// the rankUpdate() function is slower than the full product done above.
//
// On the typical bundling problems, the above product is ~5% faster.
//
// MatrixRef(blocks_[cells[c].block_id],
// col_block_size,
// col_block_size).selfadjointView<Eigen::Upper>().rankUpdate(m);
//
}
}
// Add the diagonal and invert each block.
for (int c = 0; c < bs->cols.size(); ++c) {
const int size = block_structure_.cols[c].size;
const int position = block_structure_.cols[c].position;
MatrixRef block(blocks_[c], size, size);
if (D != NULL) {
block.diagonal() += ConstVectorRef(D + position, size).array().square().matrix();
}
block = block.selfadjointView<Eigen::Upper>()
.ldlt()
.solve(Matrix::Identity(size, size));
}
}
void BlockJacobiPreconditioner::RightMultiply(const double* x, double* y) const {
for (int c = 0; c < block_structure_.cols.size(); ++c) {
const int size = block_structure_.cols[c].size;
const int position = block_structure_.cols[c].position;
ConstMatrixRef D(blocks_[c], size, size);
ConstVectorRef x_block(x + position, size);
VectorRef y_block(y + position, size);
y_block += D * x_block;
}
}
void BlockJacobiPreconditioner::LeftMultiply(const double* x, double* y) const {
RightMultiply(x, y);
}
} // namespace internal
} // namespace ceres
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