| /external/eigen/Eigen/ |
| Cholesky | 15 * - MatrixBase::ldlt()
24 #include "src/Cholesky/LDLT.h"
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| PaStiXSupport | 25 * - class PastixLDLT: a supernodal, parallel LDLt Cholesky factorization.
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| /external/eigen/test/eigen2/ |
| eigen2_cholesky.cpp | 22 LLT.h LDLT.h
69 LDLT<SquareMatrixType> ldlt(symm);
70 VERIFY(ldlt.isPositiveDefinite());
71 // in eigen3, LDLT is pivoting
72 //VERIFY_IS_APPROX(symm, ldlt.matrixL() * ldlt.vectorD().asDiagonal() * ldlt.matrixL().adjoint());
73 ldlt.solve(vecB, &vecX);
75 ldlt.solve(matB, &matX)
[all...] |
| eigen2_sparse_solvers.cpp | 112 // test LDLT
127 refMat2.ldlt().solve(b, &refX);
130 SparseLDLT<SparseSelfAdjointMatrix> ldlt(m2);
131 if (ldlt.succeeded())
132 ldlt.solveInPlace(x);
133 VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LDLT: default");
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| /external/eigen/test/ |
| cholesky.cpp | 65 LLT.h LDLT.h
134 // LDLT
146 LDLT<SquareMatrixType,Lower> ldltlo(symmLo);
153 LDLT<SquareMatrixType,Upper> ldltup(symmUp);
238 CALL_SUBTEST(( test_chol_update<SquareMatrixType,LDLT>(symm) ));
280 // LDLT
291 LDLT<RealMatrixType,Lower> ldltlo(symmLo);
312 VectorType vecX = matA.ldlt().solve(vecB);
316 // LDLT is not guaranteed to work for indefinite matrices, but happens to work fine if matrix is diagonal.
317 // This test checks that LDLT reports correctly that matrix is indefinite.
365 LDLT<MatrixType> ldlt; local
[all...] |
| nomalloc.cpp | 133 Eigen::LDLT<Matrix> LDLT; LDLT.compute(A);
134 X = LDLT.solve(B);
135 x = LDLT.solve(b);
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| /external/eigen/doc/examples/ |
| TutorialLinAlgExSolveLDLT.cpp | 14 Matrix2f x = A.ldlt().solve(b);
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| /external/eigen/Eigen/src/Cholesky/ |
| LDLT.h | 27 * \class LDLT
46 * \sa MatrixBase::ldlt(), class LLT
48 template<typename _MatrixType, int _UpLo> class LDLT
73 * perform decompositions via LDLT::compute(const MatrixType&).
75 LDLT()
86 * \sa LDLT()
88 LDLT(Index size)
99 * \sa LDLT(Index size)
101 LDLT(const MatrixType& matrix)
122 eigen_assert(m_isInitialized && "LDLT is not initialized.")
587 SelfAdjointView<MatrixType, UpLo>::ldlt() const function in class:Eigen::SelfAdjointView
597 MatrixBase<Derived>::ldlt() const function in class:Eigen::MatrixBase
[all...] |
| LLT.h | 38 * use LDLT instead for the semidefinite case. Also, do not use a Cholesky decomposition to determine whether a system of equations
44 * \sa MatrixBase::llt(), class LDLT
46 /* HEY THIS DOX IS DISABLED BECAUSE THERE's A BUG EITHER HERE OR IN LDLT ABOUT THAT (OR BOTH)
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| /external/eigen/bench/spbench/ |
| spbenchsolver.h | 130 out << " <TYPE> LDLT </TYPE> \n";
146 out << " <TYPE> LDLT </TYPE> \n";
162 out << " <TYPE> LDLT </TYPE> \n";
183 out << " <TYPE> LDLT </TYPE> \n";
391 cout << "\nSolving with Simplicial LDLT ... \n";
399 cout << "\nSolving with CHOLMOD LDLT ... \n";
409 cout << "\nSolving with PASTIX LDLT ... \n";
418 cout << "\nSolving with PARDISO LDLT ... \n";
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| spbench.dtd | 4 <!ELEMENT TYPE (#PCDATA)> <!-- One of LU, LLT, LDLT, ITER -->
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| /external/eigen/doc/ |
| TutorialLinearAlgebra.dox | 90 <td>LDLT</td>
91 <td>ldlt()</td>
101 choice is then the LDLT decomposition. Here's an example, also demonstrating that using a general
182 Another way, potentially faster but less reliable, is to use a LDLT decomposition
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| A05_PortingFrom2To3.dox | 220 <tr><td>\code A.ldlt().solve(B,&X);\endcode</td>
221 <td>\code X = A.ldlt().solve(B);
222 X = A.selfadjointView<Lower>.ldlt().solve(B);
223 X = A.selfadjointView<Upper>.ldlt().solve(B);\endcode</td>
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| TopicLinearAlgebraDecompositions.dox | 102 <td>LDLT</td>
229 <li><a name="note1">\b 1: </a>There exist two variants of the LDLT algorithm. Eigen's one produces a pure diagonal D matrix, and therefore it cannot handle indefinite matrices, unlike Lapack's one which produces a block diagonal D matrix.</li>
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| SparseLinearSystems.dox | 18 <tr><td>SimplicialLDLT </td><td>\link SparseCholesky_Module SparseCholesky \endlink</td><td>Direct LDLt factorization</td><td>SPD</td><td>Fill-in reducing</td>
34 <tr><td>PastixLLT \n PastixLDLT \n PastixLU</td><td>\link PaStiXSupport_Module PaStiXSupport \endlink</td><td>Direct LLt, LDLt, LU factorizations</td><td>SPD \n SPD \n Square</td><td>Fill-in reducing, Leverage fast dense algebra, Multithreading</td>
108 In the compute() function, the matrix is generally factorized: LLT for self-adjoint matrices, LDLT for general hermitian matrices, LU for non hermitian matrices and QR for rectangular matrices. These are the results of using direct solvers. For this class of solvers precisely, the compute step is further subdivided into analyzePattern() and factorize().
147 <TR><TH>Matrix <TH> N <TH> NNZ <TH> <TH > UMFPACK <TH > SUPERLU <TH > PASTIX LU <TH >BiCGSTAB <TH > BiCGSTAB+ILUT <TH >GMRES+ILUT<TH > LDLT <TH> CHOLMOD LDLT <TH > PASTIX LDLT <TH > LLT <TH > CHOLMOD SP LLT <TH > CHOLMOD LLT <TH > PASTIX LLT <TH> CG</TR>
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| TopicAliasing.dox | 120 <tr> <td> LDLT::solve() </td> <td> LDLT::solveInPlace() </td> </tr>
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| AsciiQuickReference.txt | 190 x = A.ldlt().solve(b)); // A sym. p.s.d. #include <Eigen/Cholesky>
195 // .ldlt() -> .matrixL() and .matrixD()
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| A10_Eigen2SupportModes.dox | 43 \li Certain fine details of linear algebraic decompositions. For example, LDLT decomposition is now pivoting in Eigen 3 whereas it wasn't in Eigen 2, so code that was relying on its underlying matrix structure will break.
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| /external/eigen/Eigen/src/SparseCholesky/ |
| SimplicialCholesky.h | 90 eigen_assert(m_isInitialized && "Simplicial LLT or LDLT is not initialized.");
104 eigen_assert(m_isInitialized && "Simplicial LLT or LDLT is not initialized.");
234 VectorType m_diag; // the diagonal coefficients (LDLT mode)
378 * \brief A direct sparse LDLT Cholesky factorizations without square root.
419 eigen_assert(Base::m_factorizationIsOk && "Simplicial LDLT not factorized");
424 eigen_assert(Base::m_factorizationIsOk && "Simplicial LDLT not factorized");
430 eigen_assert(Base::m_factorizationIsOk && "Simplicial LDLT not factorized");
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| /external/ceres-solver/internal/ceres/ |
| dense_normal_cholesky_solver.h | 73 // This class uses the LDLT factorization routines from the Eigen
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| /external/eigen/bench/ |
| benchCholesky.cpp | 59 LDLT<SquareMatrixType> cholnosqrt(covMat);
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| /external/eigen/Eigen/src/Core/ |
| SelfAdjointView.h | 153 const LDLT<PlainObject, UpLo> ldlt() const;
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| MatrixBase.h | 368 const LDLT<PlainObject> ldlt() const;
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| /external/eigen/Eigen/src/Core/util/ |
| Constants.h | 322 /** \internal Not used (meant for LDLT?). */
324 /** \internal Not used (meant for LDLT?). */
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| ForwardDeclarations.h | 226 template<typename MatrixType, int UpLo = Lower> class LDLT;
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