| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124 | // 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://// * 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 <vector>#include <algorithm>#include "ceres/internal/port.h"#include "glog/logging.h"namespace ceres {namespace internal {using std::vector;void CompressedColumnScalarMatrixToBlockMatrix(    const int* scalar_rows,    const int* scalar_cols,    const vector<int>& row_blocks,    const vector<int>& col_blocks,    vector<int>* block_rows,    vector<int>* block_cols) {  CHECK(block_rows != nullptr);  CHECK(block_cols != nullptr);  block_rows->clear();  block_cols->clear();  const int num_row_blocks = row_blocks.size();  const int num_col_blocks = col_blocks.size();  vector<int> row_block_starts(num_row_blocks);  for (int i = 0, cursor = 0; i < num_row_blocks; ++i) {    row_block_starts[i] = cursor;    cursor += row_blocks[i];  }  // 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) {      vector<int>::const_iterator it =          std::lower_bound(row_block_starts.begin(),                           row_block_starts.end(),                           scalar_rows[idx]);      // 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_block_starts.end(), but those can      // be skipped like the rows in other row blocks too.      if (it == row_block_starts.end() || *it != scalar_rows[idx]) {        continue;      }      block_rows->push_back(it - row_block_starts.begin());      ++column_size;    }    block_cols->push_back(block_cols->back() + column_size);    c += col_blocks[col_block];  }}void BlockOrderingToScalarOrdering(const vector<int>& blocks,                                   const vector<int>& block_ordering,                                   vector<int>* scalar_ordering) {  CHECK_EQ(blocks.size(), block_ordering.size());  const int num_blocks = blocks.size();  // block_starts = [0, block1, block1 + block2 ..]  vector<int> block_starts(num_blocks);  for (int i = 0, cursor = 0; i < num_blocks ; ++i) {    block_starts[i] = cursor;    cursor += blocks[i];  }  scalar_ordering->resize(block_starts.back() + blocks.back());  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];    int block_position = block_starts[block_id];    for (int j = 0; j < block_size; ++j) {      (*scalar_ordering)[cursor++] = block_position++;    }  }}}  // namespace internal}  // namespace ceres
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