block_jacobi_preconditioner.cc revision 0ae28bd5885b5daa526898fcf7c323dc2c3e1963 
1// Ceres Solver - A fast non-linear least squares minimizer
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29// Author: keir@google.com (Keir Mierle)
30
31#include "ceres/block_jacobi_preconditioner.h"
32
33#include "Eigen/Cholesky"
34#include "ceres/block_sparse_matrix.h"
35#include "ceres/block_structure.h"
36#include "ceres/casts.h"
37#include "ceres/integral_types.h"
38#include "ceres/internal/eigen.h"
39
40namespace ceres {
41namespace internal {
42
43BlockJacobiPreconditioner::BlockJacobiPreconditioner(const LinearOperator& A)
44    : num_rows_(A.num_rows()),
45      block_structure_(
46        *(down_cast<const BlockSparseMatrix*>(&A)->block_structure())) {
47  // Calculate the amount of storage needed.
48  int storage_needed = 0;
49  for (int c = 0; c < block_structure_.cols.size(); ++c) {
50    int size = block_structure_.cols[c].size;
51    storage_needed += size * size;
52  }
53
54  // Size the offsets and storage.
55  blocks_.resize(block_structure_.cols.size());
56  block_storage_.resize(storage_needed);
57
58  // Put pointers to the storage in the offsets.
59  double* block_cursor = &block_storage_[0];
60  for (int c = 0; c < block_structure_.cols.size(); ++c) {
61    int size = block_structure_.cols[c].size;
62    blocks_[c] = block_cursor;
63    block_cursor += size * size;
64  }
65}
66
67BlockJacobiPreconditioner::~BlockJacobiPreconditioner() {
68}
69
70void BlockJacobiPreconditioner::Update(const LinearOperator& matrix, const double* D) {
71  const BlockSparseMatrix& A = *(down_cast<const BlockSparseMatrix*>(&matrix));
72  const CompressedRowBlockStructure* bs = A.block_structure();
73
74  // Compute the diagonal blocks by block inner products.
75  std::fill(block_storage_.begin(), block_storage_.end(), 0.0);
76  for (int r = 0; r < bs->rows.size(); ++r) {
77    const int row_block_size = bs->rows[r].block.size;
78    const vector<Cell>& cells = bs->rows[r].cells;
79    const double* row_values = A.RowBlockValues(r);
80    for (int c = 0; c < cells.size(); ++c) {
81      const int col_block_size = bs->cols[cells[c].block_id].size;
82      ConstMatrixRef m(row_values + cells[c].position,
83                       row_block_size,
84                       col_block_size);
85
86      MatrixRef(blocks_[cells[c].block_id],
87                col_block_size,
88                col_block_size).noalias() += m.transpose() * m;
89
90      // TODO(keir): Figure out when the below expression is actually faster
91      // than doing the full rank update. The issue is that for smaller sizes,
92      // the rankUpdate() function is slower than the full product done above.
93      //
94      // On the typical bundling problems, the above product is ~5% faster.
95      //
96      //   MatrixRef(blocks_[cells[c].block_id],
97      //             col_block_size,
98      //             col_block_size).selfadjointView<Eigen::Upper>().rankUpdate(m);
99      //
100    }
101  }
102
103  // Add the diagonal and invert each block.
104  for (int c = 0; c < bs->cols.size(); ++c) {
105    const int size = block_structure_.cols[c].size;
106    const int position = block_structure_.cols[c].position;
107    MatrixRef block(blocks_[c], size, size);
108
109    if (D != NULL) {
110      block.diagonal() += ConstVectorRef(D + position, size).array().square().matrix();
111    }
112
113    block = block.selfadjointView<Eigen::Upper>()
114                 .ldlt()
115                 .solve(Matrix::Identity(size, size));
116  }
117}
118
119void BlockJacobiPreconditioner::RightMultiply(const double* x, double* y) const {
120  for (int c = 0; c < block_structure_.cols.size(); ++c) {
121    const int size = block_structure_.cols[c].size;
122    const int position = block_structure_.cols[c].position;
123    ConstMatrixRef D(blocks_[c], size, size);
124    ConstVectorRef x_block(x + position, size);
125    VectorRef y_block(y + position, size);
126    y_block += D * x_block;
127  }
128}
129
130void BlockJacobiPreconditioner::LeftMultiply(const double* x, double* y) const {
131  RightMultiply(x, y);
132}
133
134}  // namespace internal
135}  // namespace ceres
136