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  /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$. In
37 * the real case, the Hessenberg decomposition consists of an orthogonal
41 * subdiagonal, so it is almost upper triangular. The Hessenberg decomposition
45 * Call the function compute() to compute the Hessenberg decomposition of a
48 * Hessenberg decomposition at construction time. Once the decomposition is
50 * the matrices H and Q in the decomposition.
89 /** \brief Default constructor; the decomposition will be computed later.
91 * \param [in] size The size of the matrix whose Hessenberg decomposition will be computed
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Tridiagonalization.h 34 * \brief Tridiagonal decomposition of a selfadjoint matrix
37 * tridiagonal decomposition; this is expected to be an instantiation of the
40 * This class performs a tridiagonal decomposition of a selfadjoint matrix \f$ 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
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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.
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  /external/chromium_org/third_party/icu/source/data/unidata/
NormalizationCorrections.txt 11 # Consortium 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
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  /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(&quot;\u00e0\u0325 is not equal to a\u0325\u0300 without decomposition&quot;);
97 * System.out.println(&quot;Error: \u00e0\u0325 should be equal to a\u0325\u0300 with decomposition&quot;);
99 * System.out.println(&quot;\u00e0\u0325 is equal to a\u0325\u0300 with decomposition&quot;);
102 * System.out.println(&quot;Error: \u00e0\u0325 should be not equal to a\u0325\u0300 without decomposition&quot;)
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  /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
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  /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
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