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Searched
refs:SVD
(Results
1 - 16
of
16
) sorted by null
/external/eigen/doc/examples/
function_taking_ref.cpp
2
#include <Eigen/
SVD
>
/external/eigen/test/
upperbidiagonalization.cpp
11
#include <Eigen/
SVD
>
jacobi.cpp
12
#include <Eigen/
SVD
>
bdcsvd.cpp
18
#include <Eigen/
SVD
>
geo_eulerangles.cpp
13
#include <Eigen/
SVD
>
jacobisvd.cpp
15
#include <Eigen/
SVD
>
geo_orthomethods.cpp
13
#include <Eigen/
SVD
>
umeyama.cpp
16
#include <Eigen/
SVD
> // required for
SVD
geo_quaternion.cpp
14
#include <Eigen/
SVD
>
nomalloc.cpp
21
#include <Eigen/
SVD
>
155
//
SVD
module
qr_colpivoting.cpp
13
#include <Eigen/
SVD
>
56
// Verify that we get the same minimum-norm solution as the
SVD
.
57
JacobiSVD<MatrixType>
svd
(matrix, ComputeThinU | ComputeThinV);
58
MatrixType svd_solution =
svd
.solve(rhs);
88
// Verify that we get the same minimum-norm solution as the
SVD
.
89
JacobiSVD<MatrixType>
svd
(matrix, ComputeFullU | ComputeFullV);
90
Matrix<Scalar, Cols, Cols2> svd_solution =
svd
.solve(rhs);
geo_transformations.cpp
13
#include <Eigen/
SVD
>
/external/eigen/unsupported/bench/
bench_svd.cpp
13
// Bench to compare the efficiency of
SVD
algorithms
17
#include <unsupported/Eigen/
SVD
>
/external/eigen/Eigen/src/SVD/
JacobiSVD.h
23
/*** QR preconditioners (R-
SVD
)
25
*** Their role is to reduce the problem of computing the
SVD
to the case of a square matrix.
26
*** This approach, known as R-
SVD
, is an optimization for rectangular-enough matrices, and is a requirement for
76
void allocate(const JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>&
svd
)
78
if (
svd
.rows() != m_qr.rows() ||
svd
.cols() != m_qr.cols())
81
::new (&m_qr) QRType(
svd
.rows(),
svd
.cols());
83
if (
svd
.m_computeFullU) m_workspace.resize(
svd
.rows())
[
all
...]
/external/eigen/lapack/
svd.cpp
11
#include <Eigen/
SVD
>
56
BDCSVD<PlainMatrixType>
svd
(mat,option);
58
make_vector(s,diag_size) =
svd
.singularValues().head(diag_size);
62
matrix(u,*m,*m,*ldu) =
svd
.matrixU();
63
matrix(vt,*n,*n,*ldvt) =
svd
.matrixV().adjoint();
67
matrix(u,*m,diag_size,*ldu) =
svd
.matrixU();
68
matrix(vt,diag_size,*n,*ldvt) =
svd
.matrixV().adjoint();
72
matrix(a,*m,*n,*lda) =
svd
.matrixU();
73
matrix(vt,*n,*n,*ldvt) =
svd
.matrixV().adjoint();
77
matrix(u,*m,*m,*ldu) =
svd
.matrixU()
[
all
...]
/external/llvm/lib/Transforms/Instrumentation/
AddressSanitizer.cpp
[
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...]
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