-
Notifications
You must be signed in to change notification settings - Fork 3
/
Copy pathsvd.cpp
executable file
·138 lines (117 loc) · 4.78 KB
/
svd.cpp
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
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Gael Guennebaud <[email protected]>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "lapack_common.h"
#include <Eigen/SVD>
// computes the singular values/vectors a general M-by-N matrix A using divide-and-conquer
EIGEN_LAPACK_FUNC(gesdd,(char *jobz, int *m, int* n, Scalar* a, int *lda, RealScalar *s, Scalar *u, int *ldu, Scalar *vt, int *ldvt, Scalar* /*work*/, int* lwork,
EIGEN_LAPACK_ARG_IF_COMPLEX(RealScalar */*rwork*/) int * /*iwork*/, int *info))
{
// TODO exploit the work buffer
bool query_size = *lwork==-1;
int diag_size = (std::min)(*m,*n);
*info = 0;
if(*jobz!='A' && *jobz!='S' && *jobz!='O' && *jobz!='N') *info = -1;
else if(*m<0) *info = -2;
else if(*n<0) *info = -3;
else if(*lda<std::max(1,*m)) *info = -5;
else if(*lda<std::max(1,*m)) *info = -8;
else if(*ldu <1 || (*jobz=='A' && *ldu <*m)
|| (*jobz=='O' && *m<*n && *ldu<*m)) *info = -8;
else if(*ldvt<1 || (*jobz=='A' && *ldvt<*n)
|| (*jobz=='S' && *ldvt<diag_size)
|| (*jobz=='O' && *m>=*n && *ldvt<*n)) *info = -10;
if(*info!=0)
{
int e = -*info;
return xerbla_(SCALAR_SUFFIX_UP"GESDD ", &e, 6);
}
if(query_size)
{
*lwork = 0;
return 0;
}
if(*n==0 || *m==0)
return 0;
PlainMatrixType mat(*m,*n);
mat = matrix(a,*m,*n,*lda);
int option = *jobz=='A' ? ComputeFullU|ComputeFullV
: *jobz=='S' ? ComputeThinU|ComputeThinV
: *jobz=='O' ? ComputeThinU|ComputeThinV
: 0;
BDCSVD<PlainMatrixType> svd(mat,option);
make_vector(s,diag_size) = svd.singularValues().head(diag_size);
if(*jobz=='A')
{
matrix(u,*m,*m,*ldu) = svd.matrixU();
matrix(vt,*n,*n,*ldvt) = svd.matrixV().adjoint();
}
else if(*jobz=='S')
{
matrix(u,*m,diag_size,*ldu) = svd.matrixU();
matrix(vt,diag_size,*n,*ldvt) = svd.matrixV().adjoint();
}
else if(*jobz=='O' && *m>=*n)
{
matrix(a,*m,*n,*lda) = svd.matrixU();
matrix(vt,*n,*n,*ldvt) = svd.matrixV().adjoint();
}
else if(*jobz=='O')
{
matrix(u,*m,*m,*ldu) = svd.matrixU();
matrix(a,diag_size,*n,*lda) = svd.matrixV().adjoint();
}
return 0;
}
// computes the singular values/vectors a general M-by-N matrix A using two sided jacobi algorithm
EIGEN_LAPACK_FUNC(gesvd,(char *jobu, char *jobv, int *m, int* n, Scalar* a, int *lda, RealScalar *s, Scalar *u, int *ldu, Scalar *vt, int *ldvt, Scalar* /*work*/, int* lwork,
EIGEN_LAPACK_ARG_IF_COMPLEX(RealScalar */*rwork*/) int *info))
{
// TODO exploit the work buffer
bool query_size = *lwork==-1;
int diag_size = (std::min)(*m,*n);
*info = 0;
if( *jobu!='A' && *jobu!='S' && *jobu!='O' && *jobu!='N') *info = -1;
else if((*jobv!='A' && *jobv!='S' && *jobv!='O' && *jobv!='N')
|| (*jobu=='O' && *jobv=='O')) *info = -2;
else if(*m<0) *info = -3;
else if(*n<0) *info = -4;
else if(*lda<std::max(1,*m)) *info = -6;
else if(*ldu <1 || ((*jobu=='A' || *jobu=='S') && *ldu<*m)) *info = -9;
else if(*ldvt<1 || (*jobv=='A' && *ldvt<*n)
|| (*jobv=='S' && *ldvt<diag_size)) *info = -11;
if(*info!=0)
{
int e = -*info;
return xerbla_(SCALAR_SUFFIX_UP"GESVD ", &e, 6);
}
if(query_size)
{
*lwork = 0;
return 0;
}
if(*n==0 || *m==0)
return 0;
PlainMatrixType mat(*m,*n);
mat = matrix(a,*m,*n,*lda);
int option = (*jobu=='A' ? ComputeFullU : *jobu=='S' || *jobu=='O' ? ComputeThinU : 0)
| (*jobv=='A' ? ComputeFullV : *jobv=='S' || *jobv=='O' ? ComputeThinV : 0);
JacobiSVD<PlainMatrixType> svd(mat,option);
make_vector(s,diag_size) = svd.singularValues().head(diag_size);
{
if(*jobu=='A') matrix(u,*m,*m,*ldu) = svd.matrixU();
else if(*jobu=='S') matrix(u,*m,diag_size,*ldu) = svd.matrixU();
else if(*jobu=='O') matrix(a,*m,diag_size,*lda) = svd.matrixU();
}
{
if(*jobv=='A') matrix(vt,*n,*n,*ldvt) = svd.matrixV().adjoint();
else if(*jobv=='S') matrix(vt,diag_size,*n,*ldvt) = svd.matrixV().adjoint();
else if(*jobv=='O') matrix(a,diag_size,*n,*lda) = svd.matrixV().adjoint();
}
return 0;
}