1 // Ceres Solver - A fast non-linear least squares minimizer
2 // Copyright 2015 Google Inc. All rights reserved.
3 // http://ceres-solver.org/
4 //
5 // Redistribution and use in source and binary forms, with or without
6 // modification, are permitted provided that the following conditions are met:
7 //
8 // * Redistributions of source code must retain the above copyright notice,
9 // this list of conditions and the following disclaimer.
10 // * Redistributions in binary form must reproduce the above copyright notice,
11 // this list of conditions and the following disclaimer in the documentation
12 // and/or other materials provided with the distribution.
13 // * Neither the name of Google Inc. nor the names of its contributors may be
14 // used to endorse or promote products derived from this software without
15 // specific prior written permission.
16 //
17 // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
18 // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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26 // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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28 //
29 // Author: sameeragarwal@google.com (Sameer Agarwal)
30
31 #include "ceres/block_random_access_sparse_matrix.h"
32
33 #include <algorithm>
34 #include <memory>
35 #include <set>
36 #include <utility>
37 #include <vector>
38
39 #include "ceres/internal/port.h"
40 #include "ceres/triplet_sparse_matrix.h"
41 #include "ceres/types.h"
42 #include "glog/logging.h"
43
44 namespace ceres {
45 namespace internal {
46
47 using std::make_pair;
48 using std::pair;
49 using std::set;
50 using std::vector;
51
BlockRandomAccessSparseMatrix(const vector<int> & blocks,const set<pair<int,int>> & block_pairs)52 BlockRandomAccessSparseMatrix::BlockRandomAccessSparseMatrix(
53 const vector<int>& blocks, const set<pair<int, int>>& block_pairs)
54 : kMaxRowBlocks(10 * 1000 * 1000), blocks_(blocks) {
55 CHECK_LT(blocks.size(), kMaxRowBlocks);
56
57 // Build the row/column layout vector and count the number of scalar
58 // rows/columns.
59 int num_cols = 0;
60 block_positions_.reserve(blocks_.size());
61 for (int i = 0; i < blocks_.size(); ++i) {
62 block_positions_.push_back(num_cols);
63 num_cols += blocks_[i];
64 }
65
66 // Count the number of scalar non-zero entries and build the layout
67 // object for looking into the values array of the
68 // TripletSparseMatrix.
69 int num_nonzeros = 0;
70 for (const auto& block_pair : block_pairs) {
71 const int row_block_size = blocks_[block_pair.first];
72 const int col_block_size = blocks_[block_pair.second];
73 num_nonzeros += row_block_size * col_block_size;
74 }
75
76 VLOG(1) << "Matrix Size [" << num_cols << "," << num_cols << "] "
77 << num_nonzeros;
78
79 tsm_.reset(new TripletSparseMatrix(num_cols, num_cols, num_nonzeros));
80 tsm_->set_num_nonzeros(num_nonzeros);
81 int* rows = tsm_->mutable_rows();
82 int* cols = tsm_->mutable_cols();
83 double* values = tsm_->mutable_values();
84
85 int pos = 0;
86 for (const auto& block_pair : block_pairs) {
87 const int row_block_size = blocks_[block_pair.first];
88 const int col_block_size = blocks_[block_pair.second];
89 cell_values_.push_back(make_pair(block_pair, values + pos));
90 layout_[IntPairToLong(block_pair.first, block_pair.second)] =
91 new CellInfo(values + pos);
92 pos += row_block_size * col_block_size;
93 }
94
95 // Fill the sparsity pattern of the underlying matrix.
96 for (const auto& block_pair : block_pairs) {
97 const int row_block_id = block_pair.first;
98 const int col_block_id = block_pair.second;
99 const int row_block_size = blocks_[row_block_id];
100 const int col_block_size = blocks_[col_block_id];
101 int pos =
102 layout_[IntPairToLong(row_block_id, col_block_id)]->values - values;
103 for (int r = 0; r < row_block_size; ++r) {
104 for (int c = 0; c < col_block_size; ++c, ++pos) {
105 rows[pos] = block_positions_[row_block_id] + r;
106 cols[pos] = block_positions_[col_block_id] + c;
107 values[pos] = 1.0;
108 DCHECK_LT(rows[pos], tsm_->num_rows());
109 DCHECK_LT(cols[pos], tsm_->num_rows());
110 }
111 }
112 }
113 }
114
115 // Assume that the user does not hold any locks on any cell blocks
116 // when they are calling SetZero.
~BlockRandomAccessSparseMatrix()117 BlockRandomAccessSparseMatrix::~BlockRandomAccessSparseMatrix() {
118 for (const auto& entry : layout_) {
119 delete entry.second;
120 }
121 }
122
GetCell(int row_block_id,int col_block_id,int * row,int * col,int * row_stride,int * col_stride)123 CellInfo* BlockRandomAccessSparseMatrix::GetCell(int row_block_id,
124 int col_block_id,
125 int* row,
126 int* col,
127 int* row_stride,
128 int* col_stride) {
129 const LayoutType::iterator it =
130 layout_.find(IntPairToLong(row_block_id, col_block_id));
131 if (it == layout_.end()) {
132 return NULL;
133 }
134
135 // Each cell is stored contiguously as its own little dense matrix.
136 *row = 0;
137 *col = 0;
138 *row_stride = blocks_[row_block_id];
139 *col_stride = blocks_[col_block_id];
140 return it->second;
141 }
142
143 // Assume that the user does not hold any locks on any cell blocks
144 // when they are calling SetZero.
SetZero()145 void BlockRandomAccessSparseMatrix::SetZero() {
146 if (tsm_->num_nonzeros()) {
147 VectorRef(tsm_->mutable_values(), tsm_->num_nonzeros()).setZero();
148 }
149 }
150
SymmetricRightMultiply(const double * x,double * y) const151 void BlockRandomAccessSparseMatrix::SymmetricRightMultiply(const double* x,
152 double* y) const {
153 for (const auto& cell_position_and_data : cell_values_) {
154 const int row = cell_position_and_data.first.first;
155 const int row_block_size = blocks_[row];
156 const int row_block_pos = block_positions_[row];
157
158 const int col = cell_position_and_data.first.second;
159 const int col_block_size = blocks_[col];
160 const int col_block_pos = block_positions_[col];
161
162 MatrixVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(
163 cell_position_and_data.second,
164 row_block_size,
165 col_block_size,
166 x + col_block_pos,
167 y + row_block_pos);
168
169 // Since the matrix is symmetric, but only the upper triangular
170 // part is stored, if the block being accessed is not a diagonal
171 // block, then use the same block to do the corresponding lower
172 // triangular multiply also.
173 if (row != col) {
174 MatrixTransposeVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(
175 cell_position_and_data.second,
176 row_block_size,
177 col_block_size,
178 x + row_block_pos,
179 y + col_block_pos);
180 }
181 }
182 }
183
184 } // namespace internal
185 } // namespace ceres
186