| /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/doc/examples/ |
| TutorialLinAlgExSolveLDLT.cpp | 14 Matrix2f x = A.ldlt().solve(b);
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| /external/eigen/Eigen/src/Cholesky/ |
| LDLT.h | 24 * \class LDLT
43 * \sa MatrixBase::ldlt(), class LLT
45 template<typename _MatrixType, int _UpLo> class LDLT
70 * perform decompositions via LDLT::compute(const MatrixType&).
72 LDLT() : m_matrix(), m_transpositions(), m_isInitialized(false) {}
78 * \sa LDLT()
80 LDLT(Index size)
90 * \sa LDLT(Index size)
92 LDLT(const MatrixType& matrix)
112 eigen_assert(m_isInitialized && "LDLT is not initialized.")
575 SelfAdjointView<MatrixType, UpLo>::ldlt() const function in class:Eigen::SelfAdjointView
585 MatrixBase<Derived>::ldlt() const function in class:Eigen::MatrixBase
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| 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/test/ |
| cholesky.cpp | 65 LLT.h LDLT.h
119 // LDLT
131 LDLT<SquareMatrixType,Lower> ldltlo(symmLo);
138 LDLT<SquareMatrixType,Upper> ldltup(symmUp);
187 CALL_SUBTEST(( test_chol_update<SquareMatrixType,LDLT>(symm) ));
229 // LDLT
240 LDLT<RealMatrixType,Lower> ldltlo(symmLo);
261 VectorType vecX = matA.ldlt().solve(vecB);
275 LDLT<MatrixType> ldlt; local
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| nomalloc.cpp | 128 Eigen::LDLT<Matrix> LDLT; LDLT.compute(A);
129 X = LDLT.solve(B);
130 x = LDLT.solve(b);
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| /external/eigen/bench/spbench/ |
| spbenchsolver.h | 95 string LUlist =" ", LLTlist = "<TH > LLT", LDLTlist = "<TH > LDLT ";
105 LDLTlist += "<TH>CHOLMOD LDLT";
110 LDLTlist += "<TH > PARDISO LDLT";
115 LDLTlist += "<TH > PASTIX LDLT";
374 cout << "\nSolving with Simplicial LDLT ... \n";
383 cout << "\nSolving with CHOLMOD LDLT ... \n";
394 cout << "\nSolving with PASTIX LDLT ... \n";
404 cout << "\nSolving with PARDISO LDLT ... \n";
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| /external/eigen/doc/ |
| C06_TutorialLinearAlgebra.dox | 102 <td>LDLT</td>
103 <td>ldlt()</td>
113 choice is then the LDLT decomposition. Here's an example, also demonstrating that using a general
194 Another way, potentially faster but less reliable, is to use a LDLT decomposition
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| A05_PortingFrom2To3.dox | 235 <tr><td>\code A.ldlt().solve(B,&X);\endcode</td>
236 <td>\code X = A.ldlt().solve(B);
237 X = A.selfadjointView<Lower>.ldlt().solve(B);
238 X = A.selfadjointView<Upper>.ldlt().solve(B);\endcode</td>
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| TopicLinearAlgebraDecompositions.dox | 101 <td>LDLT</td>
228 <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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| AsciiQuickReference.txt | 154 solved = A.ldlt().solve(b, &x)); // A sym. p.s.d. #include <Eigen/Cholesky>
159 // .ldlt() -> .matrixL() and .matrixD()
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| I11_Aliasing.dox | 122 <tr> <td> LDLT::solve() </td> <td> LDLT::solveInPlace() </td> </tr>
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| C09_TutorialSparse.dox | 29 <tr><td>\link SparseCholesky_Module SparseCholesky \endlink</td><td>\code#include <Eigen/SparseCholesky>\endcode</td><td>Direct sparse LLT and LDLT Cholesky factorization to solve sparse self-adjoint positive definite problems</td></tr>
118 Since the resulting matrix \c A is symmetric by construction, we can perform a direct Cholesky factorization via the SimplicialLDLT class which behaves like its LDLT counterpart for dense objects.
255 <tr><td>SimplicialLDLT </td><td>\link SparseCholesky_Module SparseCholesky \endlink</td><td>Direct LDLt factorization</td><td>SPD</td><td>Fill-in reducing</td>
266 <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>
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| A10_Eigen2SupportModes.dox | 49 \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/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/SparseCholesky/ |
| SimplicialCholesky.h | 129 eigen_assert(m_isInitialized && "Simplicial LLT or LDLT is not initialized.");
143 eigen_assert(m_isInitialized && "Simplicial LLT or LDLT is not initialized.");
294 VectorType m_diag; // the diagonal coefficients (LDLT mode)
434 * \brief A direct sparse LDLT Cholesky factorizations without square root.
474 eigen_assert(Base::m_factorizationIsOk && "Simplicial LDLT not factorized");
479 eigen_assert(Base::m_factorizationIsOk && "Simplicial LDLT not factorized");
485 eigen_assert(Base::m_factorizationIsOk && "Simplicial LDLT not factorized");
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| /external/eigen/Eigen/src/Core/ |
| SelfAdjointView.h | 153 const LDLT<PlainObject, UpLo> ldlt() const;
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| MatrixBase.h | 365 const LDLT<PlainObject> ldlt() const;
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| /external/eigen/Eigen/src/Core/util/ |
| Constants.h | 315 /** \internal Not used (meant for LDLT?). */
317 /** \internal Not used (meant for LDLT?). */
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| ForwardDeclarations.h | 227 template<typename MatrixType, int UpLo = Lower> class LDLT;
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| /external/eigen/Eigen/src/CholmodSupport/ |
| CholmodSupport.h | 386 * \brief A simplicial direct Cholesky (LDLT) factorization and solver based on Cholmod
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