1 // Ceres Solver - A fast non-linear least squares minimizer
2 // Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
3 // http://code.google.com/p/ceres-solver/
4 //
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6 // modification, are permitted provided that the following conditions are met:
7 //
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9 // this list of conditions and the following disclaimer.
10 // * Redistributions in binary form must reproduce the above copyright notice,
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14 // used to endorse or promote products derived from this software without
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16 //
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28 //
29 // Author: sameeragarwal@google.com (Sameer Agarwal)
30
31 #include "ceres/implicit_schur_complement.h"
32
33 #include "Eigen/Dense"
34 #include "ceres/block_sparse_matrix.h"
35 #include "ceres/block_structure.h"
36 #include "ceres/internal/eigen.h"
37 #include "ceres/internal/scoped_ptr.h"
38 #include "ceres/linear_solver.h"
39 #include "ceres/types.h"
40 #include "glog/logging.h"
41
42 namespace ceres {
43 namespace internal {
44
ImplicitSchurComplement(const LinearSolver::Options & options)45 ImplicitSchurComplement::ImplicitSchurComplement(
46 const LinearSolver::Options& options)
47 : options_(options),
48 D_(NULL),
49 b_(NULL) {
50 }
51
~ImplicitSchurComplement()52 ImplicitSchurComplement::~ImplicitSchurComplement() {
53 }
54
Init(const BlockSparseMatrix & A,const double * D,const double * b)55 void ImplicitSchurComplement::Init(const BlockSparseMatrix& A,
56 const double* D,
57 const double* b) {
58 // Since initialization is reasonably heavy, perhaps we can save on
59 // constructing a new object everytime.
60 if (A_ == NULL) {
61 A_.reset(PartitionedMatrixViewBase::Create(options_, A));
62 }
63
64 D_ = D;
65 b_ = b;
66
67 // Initialize temporary storage and compute the block diagonals of
68 // E'E and F'E.
69 if (block_diagonal_EtE_inverse_ == NULL) {
70 block_diagonal_EtE_inverse_.reset(A_->CreateBlockDiagonalEtE());
71 if (options_.preconditioner_type == JACOBI) {
72 block_diagonal_FtF_inverse_.reset(A_->CreateBlockDiagonalFtF());
73 }
74 rhs_.resize(A_->num_cols_f());
75 rhs_.setZero();
76 tmp_rows_.resize(A_->num_rows());
77 tmp_e_cols_.resize(A_->num_cols_e());
78 tmp_e_cols_2_.resize(A_->num_cols_e());
79 tmp_f_cols_.resize(A_->num_cols_f());
80 } else {
81 A_->UpdateBlockDiagonalEtE(block_diagonal_EtE_inverse_.get());
82 if (options_.preconditioner_type == JACOBI) {
83 A_->UpdateBlockDiagonalFtF(block_diagonal_FtF_inverse_.get());
84 }
85 }
86
87 // The block diagonals of the augmented linear system contain
88 // contributions from the diagonal D if it is non-null. Add that to
89 // the block diagonals and invert them.
90 AddDiagonalAndInvert(D_, block_diagonal_EtE_inverse_.get());
91 if (options_.preconditioner_type == JACOBI) {
92 AddDiagonalAndInvert((D_ == NULL) ? NULL : D_ + A_->num_cols_e(),
93 block_diagonal_FtF_inverse_.get());
94 }
95
96 // Compute the RHS of the Schur complement system.
97 UpdateRhs();
98 }
99
100 // Evaluate the product
101 //
102 // Sx = [F'F - F'E (E'E)^-1 E'F]x
103 //
104 // By breaking it down into individual matrix vector products
105 // involving the matrices E and F. This is implemented using a
106 // PartitionedMatrixView of the input matrix A.
RightMultiply(const double * x,double * y) const107 void ImplicitSchurComplement::RightMultiply(const double* x, double* y) const {
108 // y1 = F x
109 tmp_rows_.setZero();
110 A_->RightMultiplyF(x, tmp_rows_.data());
111
112 // y2 = E' y1
113 tmp_e_cols_.setZero();
114 A_->LeftMultiplyE(tmp_rows_.data(), tmp_e_cols_.data());
115
116 // y3 = -(E'E)^-1 y2
117 tmp_e_cols_2_.setZero();
118 block_diagonal_EtE_inverse_->RightMultiply(tmp_e_cols_.data(),
119 tmp_e_cols_2_.data());
120 tmp_e_cols_2_ *= -1.0;
121
122 // y1 = y1 + E y3
123 A_->RightMultiplyE(tmp_e_cols_2_.data(), tmp_rows_.data());
124
125 // y5 = D * x
126 if (D_ != NULL) {
127 ConstVectorRef Dref(D_ + A_->num_cols_e(), num_cols());
128 VectorRef(y, num_cols()) =
129 (Dref.array().square() *
130 ConstVectorRef(x, num_cols()).array()).matrix();
131 } else {
132 VectorRef(y, num_cols()).setZero();
133 }
134
135 // y = y5 + F' y1
136 A_->LeftMultiplyF(tmp_rows_.data(), y);
137 }
138
139 // Given a block diagonal matrix and an optional array of diagonal
140 // entries D, add them to the diagonal of the matrix and compute the
141 // inverse of each diagonal block.
AddDiagonalAndInvert(const double * D,BlockSparseMatrix * block_diagonal)142 void ImplicitSchurComplement::AddDiagonalAndInvert(
143 const double* D,
144 BlockSparseMatrix* block_diagonal) {
145 const CompressedRowBlockStructure* block_diagonal_structure =
146 block_diagonal->block_structure();
147 for (int r = 0; r < block_diagonal_structure->rows.size(); ++r) {
148 const int row_block_pos = block_diagonal_structure->rows[r].block.position;
149 const int row_block_size = block_diagonal_structure->rows[r].block.size;
150 const Cell& cell = block_diagonal_structure->rows[r].cells[0];
151 MatrixRef m(block_diagonal->mutable_values() + cell.position,
152 row_block_size, row_block_size);
153
154 if (D != NULL) {
155 ConstVectorRef d(D + row_block_pos, row_block_size);
156 m += d.array().square().matrix().asDiagonal();
157 }
158
159 m = m
160 .selfadjointView<Eigen::Upper>()
161 .llt()
162 .solve(Matrix::Identity(row_block_size, row_block_size));
163 }
164 }
165
166 // Similar to RightMultiply, use the block structure of the matrix A
167 // to compute y = (E'E)^-1 (E'b - E'F x).
BackSubstitute(const double * x,double * y)168 void ImplicitSchurComplement::BackSubstitute(const double* x, double* y) {
169 const int num_cols_e = A_->num_cols_e();
170 const int num_cols_f = A_->num_cols_f();
171 const int num_cols = A_->num_cols();
172 const int num_rows = A_->num_rows();
173
174 // y1 = F x
175 tmp_rows_.setZero();
176 A_->RightMultiplyF(x, tmp_rows_.data());
177
178 // y2 = b - y1
179 tmp_rows_ = ConstVectorRef(b_, num_rows) - tmp_rows_;
180
181 // y3 = E' y2
182 tmp_e_cols_.setZero();
183 A_->LeftMultiplyE(tmp_rows_.data(), tmp_e_cols_.data());
184
185 // y = (E'E)^-1 y3
186 VectorRef(y, num_cols).setZero();
187 block_diagonal_EtE_inverse_->RightMultiply(tmp_e_cols_.data(), y);
188
189 // The full solution vector y has two blocks. The first block of
190 // variables corresponds to the eliminated variables, which we just
191 // computed via back substitution. The second block of variables
192 // corresponds to the Schur complement system, so we just copy those
193 // values from the solution to the Schur complement.
194 VectorRef(y + num_cols_e, num_cols_f) = ConstVectorRef(x, num_cols_f);
195 }
196
197 // Compute the RHS of the Schur complement system.
198 //
199 // rhs = F'b - F'E (E'E)^-1 E'b
200 //
201 // Like BackSubstitute, we use the block structure of A to implement
202 // this using a series of matrix vector products.
UpdateRhs()203 void ImplicitSchurComplement::UpdateRhs() {
204 // y1 = E'b
205 tmp_e_cols_.setZero();
206 A_->LeftMultiplyE(b_, tmp_e_cols_.data());
207
208 // y2 = (E'E)^-1 y1
209 Vector y2 = Vector::Zero(A_->num_cols_e());
210 block_diagonal_EtE_inverse_->RightMultiply(tmp_e_cols_.data(), y2.data());
211
212 // y3 = E y2
213 tmp_rows_.setZero();
214 A_->RightMultiplyE(y2.data(), tmp_rows_.data());
215
216 // y3 = b - y3
217 tmp_rows_ = ConstVectorRef(b_, A_->num_rows()) - tmp_rows_;
218
219 // rhs = F' y3
220 rhs_.setZero();
221 A_->LeftMultiplyF(tmp_rows_.data(), rhs_.data());
222 }
223
224 } // namespace internal
225 } // namespace ceres
226