Searched refs:SVD (Results 1 – 16 of 16) sorted by relevance
| /external/eigen/Eigen/ |
| D | SVD | 17 /** \defgroup SVD_Module SVD module
21 * This module provides SVD decomposition for matrices (both real and complex).
30 * #include <Eigen/SVD>
35 #include "src/SVD/UpperBidiagonalization.h"
36 #include "src/SVD/SVDBase.h"
37 #include "src/SVD/JacobiSVD.h"
38 #include "src/SVD/BDCSVD.h"
41 #include "src/SVD/JacobiSVD_LAPACKE.h"
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| D | Dense | 5 #include "SVD"
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| D | Geometry | 15 #include "SVD"
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| /external/eigen/doc/ |
| D | LeastSquares.dox | 10 The three methods discussed on this page are the SVD decomposition, the QR decomposition and normal
11 equations. Of these, the SVD decomposition is generally the most accurate but the slowest, normal
17 \section LeastSquaresSVD Using the SVD decomposition
21 this class); you also need the singular vectors but the thin SVD decomposition suffices for
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| D | B01_Experimental.dox | 23 \li SVD
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| D | TopicLinearAlgebraDecompositions.dox | 125 <td>R-SVD</td>
231 <li><a name="note2">\b 2: </a>Eigenvalues, SVD and Schur decompositions rely on iterative algorithm…
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| D | TutorialLinearAlgebra.dox | 6 QR, %SVD, eigendecompositions... After reading this page, don't miss our
186 The most accurate method to do least squares solving is with a SVD decomposition. Eigen provides one
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| D | QuickReference.dox | 21 <tr class="alt"><td>\link SVD_Module SVD \endlink</td><td>\code#include <Eigen/SVD>\endcode</td><td…
25 …clude <Eigen/Dense>\endcode</td><td>Includes Core, Geometry, LU, Cholesky, SVD, QR, and Eigenvalue…
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| D | A05_PortingFrom2To3.dox | 181 <td>SVD</td>
183 <td class="alt">We currently don't have a bidiagonalizing SVD; of course this is planned.</td>
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| D | AsciiQuickReference.txt | 202 x = A.svd() .solve(b)); // Stable, slowest. #include <Eigen/SVD>
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| D | Doxyfile.in | 218 … "svd_module=This is defined in the %SVD module. \code #include <Eigen/SVD> \endcode" \
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| /external/eigen/Eigen/src/SVD/ |
| D | JacobiSVD.h | 356 typedef JacobiSVD<MatrixType, QRPreconditioner> SVD;
358 static bool run(typename SVD::WorkMatrixType&, SVD&, Index, Index, RealScalar&) { return true; }
364 typedef JacobiSVD<MatrixType, QRPreconditioner> SVD;
367 …static bool run(typename SVD::WorkMatrixType& work_matrix, SVD& svd, Index p, Index q, RealScalar&…
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| /external/llvm/lib/Transforms/Instrumentation/ |
| D | AddressSanitizer.cpp | 2071 SmallVector<ASanStackVariableDescription, 16> SVD; in poisonStack() local
2072 SVD.reserve(AllocaVec.size()); in poisonStack()
2077 SVD.push_back(D); in poisonStack()
2083 ComputeASanStackFrameLayout(SVD, 1ULL << Mapping.Scale, MinHeaderSize, &L); in poisonStack()
2147 for (const auto &Desc : SVD) { in poisonStack()
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| /external/svox/pico_resources/tools/LingwareBuilding/PicoLingware_source_files/pkb/en-US/ |
| D | en-US_lh0_kpdf_mgc.pkb | 1911 …���R.�d�'����\G� ��� <������Dy$] 7<H_Yd�C35HI%663/(:j"{�GE7CTCUW�SVD]U�>F=YG_�LN__\VrnWo…
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| /external/libtextclassifier/models/ |
| D | textclassifier.langid.model | 369 …F�;�P]|x����$M�А�+��.�[o��>�]K��Y�0���h�*����-��c�\�b��k^E�b�� &T�r�u%SVD���.���nݨ���oq�l�n…
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| /external/libtextclassifier/tests/testdata/ |
| D | langid.model | 369 …F�;�P]|x����$M�А�+��.�[o��>�]K��Y�0���h�*����-��c�\�b��k^E�b�� &T�r�u%SVD���.���nݨ���oq�l�n…
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