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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
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//
// 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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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// 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/parallel_vector_ops.h"
#include "ceres/triplet_sparse_matrix.h"
#include "ceres/types.h"
#include "glog/logging.h"
namespace ceres::internal {
BlockRandomAccessSparseMatrix::BlockRandomAccessSparseMatrix(
const std::vector<Block>& blocks,
const std::set<std::pair<int, int>>& block_pairs,
ContextImpl* context,
int num_threads)
: blocks_(blocks), context_(context), num_threads_(num_threads) {
CHECK_LE(blocks.size(), std::numeric_limits<std::int32_t>::max());
const int num_cols = NumScalarEntries(blocks);
const int num_blocks = blocks.size();
std::vector<int> num_cells_at_row(num_blocks);
for (auto& p : block_pairs) {
++num_cells_at_row[p.first];
}
auto block_structure_ = new CompressedRowBlockStructure;
block_structure_->cols = blocks;
block_structure_->rows.resize(num_blocks);
auto p = block_pairs.begin();
int num_nonzeros = 0;
// Pairs of block indices are sorted lexicographically, thus pairs
// corresponding to a single row-block are stored in segments of index pairs
// with constant row-block index and increasing column-block index.
// CompressedRowBlockStructure is created by traversing block_pairs set.
for (int row_block_id = 0; row_block_id < num_blocks; ++row_block_id) {
auto& row = block_structure_->rows[row_block_id];
row.block = blocks[row_block_id];
row.cells.reserve(num_cells_at_row[row_block_id]);
const int row_block_size = blocks[row_block_id].size;
// Process all index pairs corresponding to the current row block. Because
// index pairs are sorted lexicographically, cells are being appended to the
// current row-block till the first change in row-block index
for (; p != block_pairs.end() && row_block_id == p->first; ++p) {
const int col_block_id = p->second;
row.cells.emplace_back(col_block_id, num_nonzeros);
num_nonzeros += row_block_size * blocks[col_block_id].size;
}
}
bsm_ = std::make_unique<BlockSparseMatrix>(block_structure_);
VLOG(1) << "Matrix Size [" << num_cols << "," << num_cols << "] "
<< num_nonzeros;
double* values = bsm_->mutable_values();
for (int row_block_id = 0; row_block_id < num_blocks; ++row_block_id) {
const auto& cells = block_structure_->rows[row_block_id].cells;
for (auto& c : cells) {
const int col_block_id = c.block_id;
double* const data = values + c.position;
layout_[IntPairToInt64(row_block_id, col_block_id)] =
std::make_unique<CellInfo>(data);
}
}
}
CellInfo* BlockRandomAccessSparseMatrix::GetCell(int row_block_id,
int col_block_id,
int* row,
int* col,
int* row_stride,
int* col_stride) {
const auto it = layout_.find(IntPairToInt64(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].size;
*col_stride = blocks_[col_block_id].size;
return it->second.get();
}
// Assume that the user does not hold any locks on any cell blocks
// when they are calling SetZero.
void BlockRandomAccessSparseMatrix::SetZero() {
bsm_->SetZero(context_, num_threads_);
}
void BlockRandomAccessSparseMatrix::SymmetricRightMultiplyAndAccumulate(
const double* x, double* y) const {
const auto bs = bsm_->block_structure();
const auto values = bsm_->values();
const int num_blocks = blocks_.size();
for (int row_block_id = 0; row_block_id < num_blocks; ++row_block_id) {
const auto& row_block = bs->rows[row_block_id];
const int row_block_size = row_block.block.size;
const int row_block_pos = row_block.block.position;
for (auto& c : row_block.cells) {
const int col_block_id = c.block_id;
const int col_block_size = blocks_[col_block_id].size;
const int col_block_pos = blocks_[col_block_id].position;
MatrixVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(
values + c.position,
row_block_size,
col_block_size,
x + col_block_pos,
y + row_block_pos);
if (col_block_id == row_block_id) {
continue;
}
// 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
MatrixTransposeVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(
values + c.position,
row_block_size,
col_block_size,
x + row_block_pos,
y + col_block_pos);
}
}
}
} // namespace ceres::internal