| // Ceres Solver - A fast non-linear least squares minimizer | 
 | // Copyright 2023 Google Inc. All rights reserved. | 
 | // http://ceres-solver.org/ | 
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 | // | 
 | // Author: sameeragarwal@google.com (Sameer Agarwal) | 
 |  | 
 | #include "ceres/compressed_col_sparse_matrix_utils.h" | 
 |  | 
 | #include <algorithm> | 
 | #include <vector> | 
 |  | 
 | #include "absl/log/check.h" | 
 | #include "ceres/internal/export.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 |