| // Ceres Solver - A fast non-linear least squares minimizer | |
| // Copyright 2015 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: | |
| // | |
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| // 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 | |
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| // specific prior written permission. | |
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| // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" | |
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| // | |
| // 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 |