| /external/eigen/Eigen/src/Cholesky/ |
| D | LDLT.h | 50 template<typename _MatrixType, int _UpLo> class LDLT
77 LDLT() in LDLT() function
90 explicit LDLT(Index size) in LDLT() function
105 explicit LDLT(const EigenBase<InputType>& matrix) in LDLT() function
122 explicit LDLT(EigenBase<InputType>& matrix) in LDLT() function
199 inline const Solve<LDLT, Rhs>
205 return Solve<LDLT, Rhs>(*this, b.derived()); in solve()
212 LDLT& compute(const EigenBase<InputType>& matrix);
224 LDLT& rankUpdate(const MatrixBase<Derived>& w, const RealScalar& alpha=1);
243 const LDLT& adjoint() const { return *this; }; in adjoint()
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| /external/eigen/test/ |
| D | cholesky.cpp | 156 LDLT<SquareMatrixType,Lower> ldltlo(symmLo); in cholesky()
173 LDLT<SquareMatrixType,Upper> ldltup(symmUp); in cholesky()
282 CALL_SUBTEST(( test_chol_update<SquareMatrixType,LDLT>(symm) )); in cholesky()
335 LDLT<RealMatrixType,Lower> ldltlo(symmLo); in cholesky_cplx()
368 LDLT<MatrixType> ldlt(2); in cholesky_definiteness()
411 LDLT<MatrixXd> ldlt; in cholesky_faillure_cases()
467 LDLT<MatrixType> ldlt; in cholesky_verify_assert()
504 CALL_SUBTEST_9( LDLT<MatrixXf>(10) ); in test_cholesky()
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| D | nomalloc.cpp | 124 Eigen::LDLT<Matrix> LDLT; LDLT.compute(A); in ctms_decompositions() local
125 X = LDLT.solve(B); in ctms_decompositions()
126 x = LDLT.solve(b); in ctms_decompositions()
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| D | inplace_decomposition.cpp | 89 CALL_SUBTEST_2(( inplace<LDLT<Ref<MatrixXd> >, MatrixXd>(true,true) )); in test_inplace_decomposition()
90 CALL_SUBTEST_2(( inplace<LDLT<Ref<Matrix4d> >, Matrix4d>(true,true) )); in test_inplace_decomposition()
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| /external/eigen/failtest/ |
| D | ldlt_int.cpp | 13 LDLT<Matrix<SCALAR,Dynamic,Dynamic> > ldlt(Matrix<SCALAR,Dynamic,Dynamic>::Random(10,10)); in main()
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| /external/eigen/doc/ |
| D | DenseDecompositionBenchmark.dox | 19 <tr class="alt"><td>LDLT</td><td>0.07 (x1.3)</td><td>0.65 (x1.5)</td><td>26.86 (x4.6)</td><td>2361.…
35 … explain why for a 4k x 4k matrix, HouseholderQR is faster than LDLT. In the future, LDLT and ColP…
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| D | TopicLinearAlgebraDecompositions.dox | 103 <td>LDLT</td>
230 <li><a name="note1">\b 1: </a>There exist two variants of the LDLT algorithm. Eigen's one produces …
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| D | SparseLinearSystems.dox | 145 In the compute() function, the matrix is generally factorized: LLT for self-adjoint matrices, LDLT …
193 … <TH >BiCGSTAB <TH > BiCGSTAB+ILUT <TH >GMRES+ILUT<TH > LDLT <TH> CHOLMOD LDLT <TH > PASTIX LDLT <…
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| D | InplaceDecomposition.dox | 105 - class LDLT
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| D | TopicAliasing.dox | 120 <tr> <td> LDLT::solve() </td> <td> LDLT::solveInPlace() </td> </tr>
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| D | TutorialLinearAlgebra.dox | 97 <td>LDLT</td>
117 choice is then the LLT or LDLT decomposition. Here's an example, also demonstrating that using a ge…
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| D | TutorialSparse.dox | 12 …code#include <Eigen/SparseCholesky>\endcode</td><td>Direct sparse LLT and LDLT Cholesky factorizat…
104 …holesky factorization via the SimplicialLDLT class which behaves like its LDLT counterpart for den…
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| D | QuickReference.dox | 19 …y \endlink</td><td>\code#include <Eigen/Cholesky>\endcode</td><td>LLT and LDLT Cholesky factorizat…
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| /external/eigen/Eigen/ |
| D | Cholesky | 32 #include "src/Cholesky/LDLT.h"
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| /external/eigen/bench/spbench/ |
| D | spbench.dtd | 4 <!ELEMENT TYPE (#PCDATA)> <!-- One of LU, LLT, LDLT, ITER -->
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| /external/eigen/bench/ |
| D | benchCholesky.cpp | 59 LDLT<SquareMatrixType> cholnosqrt(covMat); in benchLLT()
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| D | dense_solvers.cpp | 49 LDLT<MatSquare> ldlt(size); in bench()
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| /external/eigen/Eigen/src/Core/ |
| D | SelfAdjointView.h | 236 const LDLT<PlainObject, UpLo> ldlt() const;
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| D | MatrixBase.h | 358 inline const LDLT<PlainObject> ldlt() const;
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| /external/eigen/Eigen/src/Core/util/ |
| D | ForwardDeclarations.h | 261 template<typename MatrixType, int UpLo = Lower> class LDLT;
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| /external/tensorflow/third_party/eigen3/ |
| D | LICENSE | 529 ./Eigen/src/Cholesky/LDLT.h
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