// Ceres Solver - A fast non-linear least squares minimizer

// Copyright 2015 Google Inc. All rights reserved.

// http://ceres-solver.org/

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// Author: sameeragarwal@google.com (Sameer Agarwal)

#include "ceres/block_random_access_sparse_matrix.h"

#include <algorithm>

#include <set>

#include <utility>

#include <vector>

#include "ceres/internal/port.h"

#include "ceres/internal/scoped_ptr.h"

#include "ceres/mutex.h"

#include "ceres/triplet_sparse_matrix.h"

#include "ceres/types.h"

#include "glog/logging.h"

namespace ceres {

namespace internal {

using std::make_pair;

using std::pair;

using std::set;

using std::vector;

BlockRandomAccessSparseMatrix::BlockRandomAccessSparseMatrix(

    const vector<int>& blocks,

    const set<pair<int, int> >& block_pairs)

    : kMaxRowBlocks(10 * 1000 * 1000),

      blocks_(blocks) {

  CHECK_LT(blocks.size(), kMaxRowBlocks);

  // Build the row/column layout vector and count the number of scalar

  // rows/columns.

  int num_cols = 0;

  block_positions_.reserve(blocks_.size());

  for (int i = 0; i < blocks_.size(); ++i) {

    block_positions_.push_back(num_cols);

    num_cols += blocks_[i];

  }

  // Count the number of scalar non-zero entries and build the layout

  // object for looking into the values array of the

  // TripletSparseMatrix.

  int num_nonzeros = 0;

  for (set<pair<int, int> >::const_iterator it = block_pairs.begin();

       it != block_pairs.end();

       ++it) {

    const int row_block_size = blocks_[it->first];

    const int col_block_size = blocks_[it->second];

    num_nonzeros += row_block_size * col_block_size;

  }

  VLOG(1) << "Matrix Size [" << num_cols

          << "," << num_cols

          << "] " << num_nonzeros;

  tsm_.reset(new TripletSparseMatrix(num_cols, num_cols, num_nonzeros));

  tsm_->set_num_nonzeros(num_nonzeros);

  int* rows = tsm_->mutable_rows();

  int* cols = tsm_->mutable_cols();

  double* values = tsm_->mutable_values();

  int pos = 0;

  for (set<pair<int, int> >::const_iterator it = block_pairs.begin();

       it != block_pairs.end();

       ++it) {

    const int row_block_size = blocks_[it->first];

    const int col_block_size = blocks_[it->second];

    cell_values_.push_back(make_pair(make_pair(it->first, it->second),

                                     values + pos));

    layout_[IntPairToLong(it->first, it->second)] =

        new CellInfo(values + pos);

    pos += row_block_size * col_block_size;

  }

  // Fill the sparsity pattern of the underlying matrix.

  for (set<pair<int, int> >::const_iterator it = block_pairs.begin();

       it != block_pairs.end();

       ++it) {

    const int row_block_id = it->first;

    const int col_block_id = it->second;

    const int row_block_size = blocks_[row_block_id];

    const int col_block_size = blocks_[col_block_id];

    int pos =

        layout_[IntPairToLong(row_block_id, col_block_id)]->values - values;

    for (int r = 0; r < row_block_size; ++r) {

      for (int c = 0; c < col_block_size; ++c, ++pos) {

          rows[pos] = block_positions_[row_block_id] + r;

          cols[pos] = block_positions_[col_block_id] + c;

          values[pos] = 1.0;

          DCHECK_LT(rows[pos], tsm_->num_rows());

          DCHECK_LT(cols[pos], tsm_->num_rows());

      }

    }

  }

}

// Assume that the user does not hold any locks on any cell blocks

// when they are calling SetZero.

BlockRandomAccessSparseMatrix::~BlockRandomAccessSparseMatrix() {

  for (LayoutType::iterator it = layout_.begin();

       it != layout_.end();

       ++it) {

    delete it->second;

  }

}

CellInfo* BlockRandomAccessSparseMatrix::GetCell(int row_block_id,

                                                 int col_block_id,

                                                 int* row,

                                                 int* col,

                                                 int* row_stride,

                                                 int* col_stride) {

  const LayoutType::iterator it  =

      layout_.find(IntPairToLong(row_block_id, col_block_id));

  if (it == layout_.end()) {

    return NULL;

  }

  // Each cell is stored contiguously as its own little dense matrix.

  *row = 0;

  *col = 0;

  *row_stride = blocks_[row_block_id];

  *col_stride = blocks_[col_block_id];

  return it->second;

}

// Assume that the user does not hold any locks on any cell blocks

// when they are calling SetZero.

void BlockRandomAccessSparseMatrix::SetZero() {

  if (tsm_->num_nonzeros()) {

    VectorRef(tsm_->mutable_values(),

              tsm_->num_nonzeros()).setZero();

  }

}

void BlockRandomAccessSparseMatrix::SymmetricRightMultiply(const double* x,

                                                           double* y) const {

  vector< pair<pair<int, int>, double*> >::const_iterator it =

      cell_values_.begin();

  for (; it != cell_values_.end(); ++it) {

    const int row = it->first.first;

    const int row_block_size = blocks_[row];

    const int row_block_pos = block_positions_[row];

    const int col = it->first.second;

    const int col_block_size = blocks_[col];

    const int col_block_pos = block_positions_[col];

    MatrixVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(

        it->second, row_block_size, col_block_size,

        x + col_block_pos,

        y + row_block_pos);

    // Since the matrix is symmetric, but only the upper triangular

    // part is stored, if the block being accessed is not a diagonal

    // block, then use the same block to do the corresponding lower

    // triangular multiply also.

    if (row != col) {

      MatrixTransposeVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(

          it->second, row_block_size, col_block_size,

          x + row_block_pos,

          y + col_block_pos);

    }

  }

}

}  // namespace internal

}  // namespace ceres