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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2022 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
// 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: sameeragarwal@google.com (Sameer Agarwal)
#include "ceres/block_random_access_sparse_matrix.h"
#include <algorithm>
#include <memory>
#include <set>
#include <utility>
#include <vector>
#include "ceres/internal/export.h"
#include "ceres/triplet_sparse_matrix.h"
#include "ceres/types.h"
#include "glog/logging.h"
namespace ceres {
namespace internal {
using std::make_pair;
using std::pair;
using std::set;
using std::vector;
BlockRandomAccessSparseMatrix::BlockRandomAccessSparseMatrix(
const vector<int>& blocks, const set<pair<int, int>>& block_pairs)
: kMaxRowBlocks(10 * 1000 * 1000), blocks_(blocks) {
CHECK_LT(blocks.size(), kMaxRowBlocks);
// Build the row/column layout vector and count the number of scalar
// rows/columns.
int num_cols = 0;
block_positions_.reserve(blocks_.size());
for (int block_size : blocks_) {
block_positions_.push_back(num_cols);
num_cols += block_size;
}
// Count the number of scalar non-zero entries and build the layout
// object for looking into the values array of the
// TripletSparseMatrix.
int num_nonzeros = 0;
for (const auto& block_pair : block_pairs) {
const int row_block_size = blocks_[block_pair.first];
const int col_block_size = blocks_[block_pair.second];
num_nonzeros += row_block_size * col_block_size;
}
VLOG(1) << "Matrix Size [" << num_cols << "," << num_cols << "] "
<< num_nonzeros;
tsm_ =
std::make_unique<TripletSparseMatrix>(num_cols, num_cols, num_nonzeros);
tsm_->set_num_nonzeros(num_nonzeros);
int* rows = tsm_->mutable_rows();
int* cols = tsm_->mutable_cols();
double* values = tsm_->mutable_values();
int pos = 0;
for (const auto& block_pair : block_pairs) {
const int row_block_size = blocks_[block_pair.first];
const int col_block_size = blocks_[block_pair.second];
cell_values_.emplace_back(block_pair, values + pos);
layout_[IntPairToLong(block_pair.first, block_pair.second)] =
new CellInfo(values + pos);
pos += row_block_size * col_block_size;
}
// Fill the sparsity pattern of the underlying matrix.
for (const auto& block_pair : block_pairs) {
const int row_block_id = block_pair.first;
const int col_block_id = block_pair.second;
const int row_block_size = blocks_[row_block_id];
const int col_block_size = blocks_[col_block_id];
int pos =
layout_[IntPairToLong(row_block_id, col_block_id)]->values - values;
for (int r = 0; r < row_block_size; ++r) {
for (int c = 0; c < col_block_size; ++c, ++pos) {
rows[pos] = block_positions_[row_block_id] + r;
cols[pos] = block_positions_[col_block_id] + c;
values[pos] = 1.0;
DCHECK_LT(rows[pos], tsm_->num_rows());
DCHECK_LT(cols[pos], tsm_->num_rows());
}
}
}
}
// Assume that the user does not hold any locks on any cell blocks
// when they are calling SetZero.
BlockRandomAccessSparseMatrix::~BlockRandomAccessSparseMatrix() {
for (const auto& entry : layout_) {
delete entry.second;
}
}
CellInfo* BlockRandomAccessSparseMatrix::GetCell(int row_block_id,
int col_block_id,
int* row,
int* col,
int* row_stride,
int* col_stride) {
const LayoutType::iterator it =
layout_.find(IntPairToLong(row_block_id, col_block_id));
if (it == layout_.end()) {
return nullptr;
}
// Each cell is stored contiguously as its own little dense matrix.
*row = 0;
*col = 0;
*row_stride = blocks_[row_block_id];
*col_stride = blocks_[col_block_id];
return it->second;
}
// Assume that the user does not hold any locks on any cell blocks
// when they are calling SetZero.
void BlockRandomAccessSparseMatrix::SetZero() {
if (tsm_->num_nonzeros()) {
VectorRef(tsm_->mutable_values(), tsm_->num_nonzeros()).setZero();
}
}
void BlockRandomAccessSparseMatrix::SymmetricRightMultiply(const double* x,
double* y) const {
for (const auto& cell_position_and_data : cell_values_) {
const int row = cell_position_and_data.first.first;
const int row_block_size = blocks_[row];
const int row_block_pos = block_positions_[row];
const int col = cell_position_and_data.first.second;
const int col_block_size = blocks_[col];
const int col_block_pos = block_positions_[col];
MatrixVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(
cell_position_and_data.second,
row_block_size,
col_block_size,
x + col_block_pos,
y + row_block_pos);
// Since the matrix is symmetric, but only the upper triangular
// part is stored, if the block being accessed is not a diagonal
// block, then use the same block to do the corresponding lower
// triangular multiply also.
if (row != col) {
MatrixTransposeVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(
cell_position_and_data.second,
row_block_size,
col_block_size,
x + row_block_pos,
y + col_block_pos);
}
}
}
} // namespace internal
} // namespace ceres
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