OpenGrok
Home
Sort by relevance
Sort by last modified time
Full Search
Definition
Symbol
File Path
History
|
|
Help
Searched
full:decomposition
(Results
1 - 25
of
292
) sorted by null
1
2
3
4
5
6
7
8
9
10
11
>>
/external/eigen/doc/snippets/
HessenbergDecomposition_compute.cpp
4
cout << "The matrix H in the
decomposition
of A is:" << endl << hd.matrixH() << endl;
5
hd.compute(2*A); // re-use hd to compute and store
decomposition
of 2A
6
cout << "The matrix H in the
decomposition
of 2A is:" << endl << hd.matrixH() << endl;
ComplexSchur_compute.cpp
4
cout << "The matrix T in the
decomposition
of A is:" << endl << schur.matrixT() << endl;
6
cout << "The matrix T in the
decomposition
of A^(-1) is:" << endl << schur.matrixT() << endl;
RealSchur_compute.cpp
4
cout << "The matrix T in the
decomposition
of A is:" << endl << schur.matrixT() << endl;
6
cout << "The matrix T in the
decomposition
of A^(-1) is:" << endl << schur.matrixT() << endl;
Tridiagonalization_compute.cpp
5
cout << "The matrix T in the tridiagonal
decomposition
of A is: " << endl;
8
cout << "The matrix T in the tridiagonal
decomposition
of 2A is: " << endl;
LLT_example.cpp
5
LLT<MatrixXd> lltOfA(A); // compute the Cholesky
decomposition
of A
6
MatrixXd L = lltOfA.matrixL(); // retrieve factor L in the
decomposition
Tutorial_solve_reuse_decomposition.cpp
3
PartialPivLU<Matrix3f> luOfA(A); // compute LU
decomposition
of A
/external/eigen/Eigen/
SparseQR
9
* \brief Provides QR
decomposition
for sparse matrices
11
* This module provides a simplicial version of the left-looking Sparse QR
decomposition
.
13
*
decomposition
. Built-in methods (COLAMD, AMD) or external methods (METIS) can be used to this end.
SVD
14
* This module provides SVD
decomposition
for matrices (both real and complex).
15
* This
decomposition
is accessible via the following MatrixBase method:
OrderingMethods
15
* the sparse matrix
decomposition
(LLT, LU, QR).
18
* Using for instance the sparse Cholesky
decomposition
, it is expected that
27
* A simple usage is as a template parameter in the sparse
decomposition
classes :
Cholesky
12
* This module provides two variants of the Cholesky
decomposition
for selfadjoint (hermitian) matrices.
/external/eigen/bench/btl/data/
action_settings.txt
11
cholesky ; "{/*1.5 Cholesky
decomposition
}" ; "matrix size" ; 4:3000
12
complete_lu_decomp ; "{/*1.5 Complete LU
decomposition
}" ; "matrix size" ; 4:3000
13
partial_lu_decomp ; "{/*1.5 Partial LU
decomposition
}" ; "matrix size" ; 4:3000
15
hessenberg ; "{/*1.5 Hessenberg
decomposition
}" ; "matrix size" ; 4:3000
/external/eigen/doc/examples/
TutorialLinAlgComputeTwice.cpp
15
cout << "Computing LLT
decomposition
..." << endl;
20
cout << "Computing LLT
decomposition
..." << endl;
/external/eigen/unsupported/Eigen/
SVD
14
* This module provides SVD
decomposition
for matrices (both real and complex).
15
* This
decomposition
is accessible via the following MatrixBase method:
/external/eigen/Eigen/src/Eigenvalues/
HessenbergDecomposition.h
34
* \tparam _MatrixType the type of the matrix of which we are computing the Hessenberg
decomposition
36
* This class performs an Hessenberg
decomposition
of a matrix \f$ A \f$. In
37
* the real case, the Hessenberg
decomposition
consists of an orthogonal
41
* subdiagonal, so it is almost upper triangular. The Hessenberg
decomposition
45
* Call the function compute() to compute the Hessenberg
decomposition
of a
48
* Hessenberg
decomposition
at construction time. Once the
decomposition
is
50
* the matrices H and Q in the
decomposition
.
89
/** \brief Default constructor; the
decomposition
will be computed later.
91
* \param [in] size The size of the matrix whose Hessenberg
decomposition
will be computed
[
all
...]
Tridiagonalization.h
34
* \brief Tridiagonal
decomposition
of a selfadjoint matrix
37
* tridiagonal
decomposition
; this is expected to be an instantiation of the
40
* This class performs a tridiagonal
decomposition
of a selfadjoint matrix \f$ A \f$ such that:
45
*
decomposition
of a selfadjoint matrix is in fact a tridiagonal
46
*
decomposition
. This class is used in SelfAdjointEigenSolver to compute the
49
* Call the function compute() to compute the tridiagonal
decomposition
of a
51
* constructor which computes the tridiagonal Schur
decomposition
at
52
* construction time. Once the
decomposition
is computed, you can use the
54
*
decomposition
.
104
*
decomposition
will be computed
[
all
...]
ComplexSchur.h
28
* \brief Performs a complex Schur
decomposition
of a real or complex square matrix
31
* computing the Schur
decomposition
; this is expected to be an
35
* Schur
decomposition
: \f$ A = U T U^*\f$ where U is a unitary
40
* Call the function compute() to compute the Schur
decomposition
of
43
* the Schur
decomposition
at construction time. Once the
44
*
decomposition
is computed, you can use the matrixU() and matrixT()
45
* functions to retrieve the matrices U and V in the
decomposition
.
76
/** \brief Type for the matrices in the Schur
decomposition
.
85
* \param [in] size Positive integer, size of the matrix whose Schur
decomposition
will be computed.
103
/** \brief Constructor; computes Schur
decomposition
of given matrix.
[
all
...]
/external/chromium_org/third_party/icu/source/data/unidata/
NormalizationCorrections.txt
11
# ordinarily precludes any change to the
decomposition
14
# exceptional (and rare) conditions, an error in a
decomposition
34
# Field 1: Original (erroneous)
decomposition
35
# Field 2: Corrected
decomposition
/external/eigen/doc/
TutorialLinearAlgebra.dox
35
Here, ColPivHouseholderQR is a QR
decomposition
with column pivoting. It's a good compromise for this tutorial, as it
41
<th>
Decomposition
</th>
101
choice is then the LDLT
decomposition
. Here's an example, also demonstrating that using a general
157
allows Eigen to avoid performing a LU
decomposition
, and instead use formulas that are more efficient on such small matrices.
170
The best way to do least squares solving is with a SVD
decomposition
. Eigen provides one as the JacobiSVD class, and its solve()
182
Another way, potentially faster but less reliable, is to use a LDLT
decomposition
188
In the above examples, the
decomposition
was computed at the same time that the
decomposition
object was constructed.
191
decomposition
object.
196
on an already-computed
decomposition
, reinitializing it
[
all
...]
/external/icu/icu4c/source/data/unidata/
NormalizationCorrections.txt
11
# ordinarily precludes any change to the
decomposition
14
# exceptional (and rare) conditions, an error in a
decomposition
34
# Field 1: Original (erroneous)
decomposition
35
# Field 2: Corrected
decomposition
/libcore/luni/src/main/java/java/text/
Normalizer.java
37
* Normalization Form D - Canonical
Decomposition
.
42
* Normalization Form C - Canonical
Decomposition
, followed by Canonical Composition.
47
* Normalization Form KD - Compatibility
Decomposition
.
52
* Normalization Form KC - Compatibility
Decomposition
, followed by Canonical Composition.
Collator.java
61
* This {@code Collator} deals only with two
decomposition
modes, the canonical
62
*
decomposition
mode and one that does not use any
decomposition
. The
63
* compatibility
decomposition
mode
65
* canonical
decomposition
mode is set, {@code Collator} handles un-normalized
67
* NFD. If canonical
decomposition
is turned off, it is the user's
94
* System.out.println("\u00e0\u0325 is not equal to a\u0325\u0300 without
decomposition
");
97
* System.out.println("Error: \u00e0\u0325 should be equal to a\u0325\u0300 with
decomposition
");
99
* System.out.println("\u00e0\u0325 is equal to a\u0325\u0300 with
decomposition
");
102
* System.out.println("Error: \u00e0\u0325 should be not equal to a\u0325\u0300 without
decomposition
")
[
all
...]
/external/eigen/Eigen/src/LU/
FullPivLU.h
19
* \brief LU
decomposition
of a matrix with complete pivoting, and related features
21
* \param MatrixType the type of the matrix of which we are computing the LU
decomposition
23
* This class represents a LU
decomposition
of any matrix, with complete pivoting: the matrix A is
26
*
decomposition
. The eigenvalues (diagonal coefficients) of U are sorted in such a way that any
29
* This
decomposition
provides the generic approach to solving systems of linear equations, computing
32
* This LU
decomposition
is very stable and well tested with large matrices. However there are use cases where the SVD
33
*
decomposition
is inherently more stable and/or flexible. For example, when computing the kernel of a matrix,
35
* the LU
decomposition
doesn't see.
37
* The data of the LU
decomposition
can be directly accessed through the methods matrixLU(),
84
* \param matrix the matrix of which to compute the LU
decomposition
[
all
...]
/external/chromium_org/third_party/harfbuzz-ng/src/
hb-unicode.h
253
* @decomposed: address of codepoint array (of length %HB_UNICODE_MAX_DECOMPOSITION_LEN) to write
decomposition
into
256
* Fully decompose @u to its Unicode compatibility
decomposition
. The codepoints of the
decomposition
will be written to @decomposed.
257
* The complete length of the
decomposition
will be returned.
259
* If @u has no compatibility
decomposition
, zero should be returned.
262
* compatibility
decomposition
plus an terminating value of 0. Consequently, @decompose must be allocated by the caller to be at least this length. Implementations
265
* Return value: number of codepoints in the full compatibility
decomposition
of @u, or 0 if no
decomposition
available.
272
/* See Unicode 6.1 for details on the maximum
decomposition
length. */
/external/harfbuzz_ng/src/
hb-unicode.h
253
* @decomposed: address of codepoint array (of length %HB_UNICODE_MAX_DECOMPOSITION_LEN) to write
decomposition
into
256
* Fully decompose @u to its Unicode compatibility
decomposition
. The codepoints of the
decomposition
will be written to @decomposed.
257
* The complete length of the
decomposition
will be returned.
259
* If @u has no compatibility
decomposition
, zero should be returned.
262
* compatibility
decomposition
plus an terminating value of 0. Consequently, @decompose must be allocated by the caller to be at least this length. Implementations
265
* Return value: number of codepoints in the full compatibility
decomposition
of @u, or 0 if no
decomposition
available.
272
/* See Unicode 6.1 for details on the maximum
decomposition
length. */
/external/eigen/Eigen/src/Cholesky/
LLT.h
23
* \brief Standard Cholesky
decomposition
(LL^T) of a matrix and associated features
25
* \param MatrixType the type of the matrix of which we are computing the LL^T Cholesky
decomposition
29
* This class performs a LL^T Cholesky
decomposition
of a symmetric, positive definite
32
* While the Cholesky
decomposition
is particularly useful to solve selfadjoint problems like D^*D x = b,
33
* for that purpose, we recommend the Cholesky
decomposition
without square root which is more stable
34
* and even faster. Nevertheless, this standard Cholesky
decomposition
remains useful in many other
37
* Remember that Cholesky decompositions are not rank-revealing. This LLT
decomposition
is only stable on positive definite matrices,
38
* use LDLT instead for the semidefinite case. Also, do not use a Cholesky
decomposition
to determine whether a system of equations
47
* Note that during the
decomposition
, only the upper triangular part of A is considered. Therefore,
110
/** \returns the solution x of \f$ A x = b \f$ using the current
decomposition
of A
[
all
...]
Completed in 820 milliseconds
1
2
3
4
5
6
7
8
9
10
11
>>