1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
|
// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2017 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)
#ifndef CERES_INTERNAL_INNER_PRODUCT_COMPUTER_H_
#define CERES_INTERNAL_INNER_PRODUCT_COMPUTER_H_
#include <memory>
#include <vector>
#include "ceres/block_sparse_matrix.h"
#include "ceres/compressed_row_sparse_matrix.h"
#include "ceres/internal/disable_warnings.h"
#include "ceres/internal/export.h"
namespace ceres {
namespace internal {
// This class is used to repeatedly compute the inner product
//
// result = m' * m
//
// where the sparsity structure of m remains constant across calls.
//
// Upon creation, the class computes and caches information needed to
// compute v, and then uses it to efficiently compute the product
// every time InnerProductComputer::Compute is called.
//
// See sparse_normal_cholesky_solver.cc for example usage.
//
// Note that the result matrix is a block upper or lower-triangular
// matrix, i.e., it will contain entries in the upper or lower
// triangular part of the matrix corresponding to the block that occur
// along its diagonal.
//
// This is not a problem as sparse linear algebra libraries can ignore
// these entries with ease and the space used is minimal/linear in the
// size of the matrices.
class CERES_NO_EXPORT InnerProductComputer {
public:
// Factory
//
// m is the input matrix
//
// Since m' * m is a symmetric matrix, we only compute half of the
// matrix and the value of storage_type which must be
// UPPER_TRIANGULAR or LOWER_TRIANGULAR determines which half is
// computed.
//
// The user must ensure that the matrix m is valid for the life time
// of this object.
static std::unique_ptr<InnerProductComputer> Create(
const BlockSparseMatrix& m,
CompressedRowSparseMatrix::StorageType storage_type);
// This factory method allows the user control over range of row
// blocks of m that should be used to compute the inner product.
//
// a = m(start_row_block : end_row_block, :);
// result = a' * a;
static std::unique_ptr<InnerProductComputer> Create(
const BlockSparseMatrix& m,
int start_row_block,
int end_row_block,
CompressedRowSparseMatrix::StorageType storage_type);
// Update result_ to be numerically equal to m' * m.
void Compute();
// Accessors for the result containing the inner product.
//
// Compute must be called before accessing this result for
// the first time.
const CompressedRowSparseMatrix& result() const { return *result_; }
CompressedRowSparseMatrix* mutable_result() const { return result_.get(); }
private:
// A ProductTerm is a term in the block inner product of a matrix
// with itself.
struct ProductTerm {
ProductTerm(const int row, const int col, const int index)
: row(row), col(col), index(index) {}
bool operator<(const ProductTerm& right) const {
if (row == right.row) {
if (col == right.col) {
return index < right.index;
}
return col < right.col;
}
return row < right.row;
}
int row;
int col;
int index;
};
InnerProductComputer(const BlockSparseMatrix& m,
int start_row_block,
int end_row_block);
void Init(CompressedRowSparseMatrix::StorageType storage_type);
std::unique_ptr<CompressedRowSparseMatrix> CreateResultMatrix(
const CompressedRowSparseMatrix::StorageType storage_type,
int num_nonzeros);
int ComputeNonzeros(const std::vector<ProductTerm>& product_terms,
std::vector<int>* row_block_nnz);
void ComputeOffsetsAndCreateResultMatrix(
const CompressedRowSparseMatrix::StorageType storage_type,
const std::vector<ProductTerm>& product_terms);
const BlockSparseMatrix& m_;
const int start_row_block_;
const int end_row_block_;
std::unique_ptr<CompressedRowSparseMatrix> result_;
// For each term in the inner product, result_offsets_ contains the
// location in the values array of the result_ matrix where it
// should be stored.
//
// This is the principal look up table that allows this class to
// compute the inner product fast.
std::vector<int> result_offsets_;
};
} // namespace internal
} // namespace ceres
#include "ceres/internal/reenable_warnings.h"
#endif // CERES_INTERNAL_INNER_PRODUCT_COMPUTER_H_
|
