Searched refs:Cholesky (Results 1 – 25 of 28) sorted by relevance
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8 /** \defgroup Cholesky_Module Cholesky module12 …* This module provides two variants of the Cholesky decomposition for selfadjoint (hermitian) matr…18 * #include <Eigen/Cholesky>23 #include "src/Cholesky/LLT.h"24 #include "src/Cholesky/LDLT.h"26 #include "src/Cholesky/LLT_MKL.h"
24 * - class PastixLLT : a supernodal, parallel LLt Cholesky factorization.25 * - class PastixLDLT: a supernodal, parallel LDLt Cholesky factorization.
17 * - class CholmodSupernodalLLT: a supernodal LLT Cholesky factorization.18 …* - class CholmodDecomposiiton: a general L(D)LT Cholesky factorization with automatic or explicit…
3 #include "Cholesky"
8 #include "Cholesky"
21 …* This module currently provides two variants of the direct sparse Cholesky decomposition for self…
18 * Using for instance the sparse Cholesky decomposition, it is expected that
5 DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Cholesky COMPONENT Devel
44 - Dense QR and Cholesky factorization (using Eigen) for small problems45 - Sparse Cholesky factorization (using SuiteSparse) for large sparse problems
11 cholesky ; "{/*1.5 Cholesky decomposition}" ; "matrix size" ; 4:3000
184 LinearSolverTerminationType Cholesky(cholmod_sparse* A,
248 LinearSolverTerminationType SuiteSparse::Cholesky(cholmod_sparse* A, in Cholesky() function in ceres::internal::SuiteSparse
357 summary.termination_type = ss_.Cholesky(&lhs, factor_, &summary.message); in SolveImplUsingSuiteSparse()
344 ss_.Cholesky(cholmod_lhs, factor_, &summary.message); in SolveReducedLinearSystemUsingSuiteSparse()
451 ? ss_.Cholesky(lhs, factor_, &status) in Factorize()
50 solvers - dense QR and dense Cholesky factorization (using51 `Eigen`_ or `LAPACK`_) for dense problems, sparse Cholesky
29 Cholesky Factorization and Update/Downdate**, *TOMS*, 35(3), 2008.
182 :eq:`lsqr` using a Cholesky or a QR factorization and lead to an exact469 R^\top R` be the Cholesky factorization of the normal equations, where478 The observant reader will note that the :math:`R` in the Cholesky482 Q^\top Q R = R^\top R`. There are two variants of Cholesky486 Cholesky factorization of the normal equations. Ceres uses490 Cholesky factorization of the normal equations. This leads to492 problems. Ceres uses the sparse Cholesky factorization routines in494 or the sparse Cholesky factorization algorithm in ``Eigen`` (which577 exactly is via the Cholesky factorization [TrefethenBau]_ and590 the sparsity of the Cholesky decomposition, and focus their compute[all …]
25 \li Cholesky
19 …tr><td>\link Cholesky_Module Cholesky \endlink</td><td>\code#include <Eigen/Cholesky>\endcode</td>…25 <tr><td></td><td>\code#include <Eigen/Dense>\endcode</td><td>Includes Core, Geometry, LU, Cholesky,…659 // via a standard Cholesky factorization661 // via a Cholesky factorization with pivoting
190 x = A.ldlt().solve(b)); // A sym. p.s.d. #include <Eigen/Cholesky>191 x = A.llt() .solve(b)); // A sym. p.d. #include <Eigen/Cholesky>
97 <tr class="alt"><td>Cholesky decomposition \n \c EIGEN_USE_LAPACKE \n \c EIGEN_USE_LAPACKE_STRICT <…
12 …clude <Eigen/SparseCholesky>\endcode</td><td>Direct sparse LLT and LDLT Cholesky factorization to …104 Since the resulting matrix \c A is symmetric by construction, we can perform a direct Cholesky fact…
114 as the Simplicial Cholesky factorization in Eigen is licensed under the LGPL.252 MESSAGE(" Ceres Solver as the Simplicial Cholesky factorization in Eigen")