// 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 #include #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& row_blocks, const std::vector& col_blocks, std::vector* block_rows, std::vector* 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& blocks, const std::vector& block_ordering, std::vector* 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