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- // Ceres Solver - A fast non-linear least squares minimizer
- // Copyright 2023 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/compressed_col_sparse_matrix_utils.h"
- #include <algorithm>
- #include <vector>
- #include "ceres/internal/export.h"
- #include "glog/logging.h"
- namespace ceres::internal {
- void CompressedColumnScalarMatrixToBlockMatrix(
- const int* scalar_rows,
- const int* scalar_cols,
- const std::vector<Block>& row_blocks,
- const std::vector<Block>& col_blocks,
- std::vector<int>* block_rows,
- std::vector<int>* block_cols) {
- CHECK(block_rows != nullptr);
- CHECK(block_cols != nullptr);
- block_rows->clear();
- block_cols->clear();
- const int num_col_blocks = col_blocks.size();
- // This loop extracts the block sparsity of the scalar sparse matrix
- // It does so by iterating over the columns, but only considering
- // the columns corresponding to the first element of each column
- // block. Within each column, the inner loop iterates over the rows,
- // and detects the presence of a row block by checking for the
- // presence of a non-zero entry corresponding to its first element.
- block_cols->push_back(0);
- int c = 0;
- for (int col_block = 0; col_block < num_col_blocks; ++col_block) {
- int column_size = 0;
- for (int idx = scalar_cols[c]; idx < scalar_cols[c + 1]; ++idx) {
- auto it = std::lower_bound(row_blocks.begin(),
- row_blocks.end(),
- scalar_rows[idx],
- [](const Block& block, double value) {
- return block.position < value;
- });
- // Since we are using lower_bound, it will return the row id where the row
- // block starts. For everything but the first row of the block, where
- // these values will be the same, we can skip, as we only need the first
- // row to detect the presence of the block.
- //
- // For rows all but the first row in the last row block, lower_bound will
- // return row_blocks_.end(), but those can be skipped like the rows in
- // other row blocks too.
- if (it == row_blocks.end() || it->position != scalar_rows[idx]) {
- continue;
- }
- block_rows->push_back(it - row_blocks.begin());
- ++column_size;
- }
- block_cols->push_back(block_cols->back() + column_size);
- c += col_blocks[col_block].size;
- }
- }
- void BlockOrderingToScalarOrdering(const std::vector<Block>& blocks,
- const std::vector<int>& block_ordering,
- std::vector<int>* scalar_ordering) {
- CHECK_EQ(blocks.size(), block_ordering.size());
- const int num_blocks = blocks.size();
- scalar_ordering->resize(NumScalarEntries(blocks));
- int cursor = 0;
- for (int i = 0; i < num_blocks; ++i) {
- const int block_id = block_ordering[i];
- const int block_size = blocks[block_id].size;
- int block_position = blocks[block_id].position;
- for (int j = 0; j < block_size; ++j) {
- (*scalar_ordering)[cursor++] = block_position++;
- }
- }
- }
- } // namespace ceres::internal
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