Breakdown-free version of ILU factorization for nonsymmetric positive definite matrices
نویسندگان
چکیده
منابع مشابه
A Sparse Approximate Inverse Preconditioner for Nonsymmetric Positive Definite Matrices
We develop an algorithm for computing a sparse approximate inverse for a nonsymmetric positive definite matrix based upon the FFAPINV algorithm. The sparse approximate inverse is computed in the factored form and used to work with some Krylov subspace methods. The preconditioner is breakdown free and, when used in conjunction with Krylovsubspace-based iterative solvers such as the GMRES algorit...
متن کاملA robust incomplete factorization preconditioner for positive definite matrices
We describe a novel technique for computing a sparse incomplete factorization of a general symmetric positive de nite matrix A. The factorization is not based on the Cholesky algorithm (or Gaussian elimination), but on A-orthogonalization. Thus, the incomplete factorization always exists and can be computed without any diagonal modi cation. When used in conjunction with the conjugate gradient a...
متن کاملAn Incomplete Cholesky Factorization for Dense Symmetric Positive Definite Matrices
In this paper, we study the use of an incomplete Cholesky factorization (ICF) as a preconditioner for solving dense symmetric positive definite linear systems. This method is suitable for situations where matrices cannot be explicitly stored but each column can be easily computed. Analysis and implementation of this preconditioner are discussed. We test the proposed ICF on randomly generated sy...
متن کاملOrderings for ILU Preconditioning of Nonsymmetric Problems
Numerical experiments are presented whereby the eeect of reorderings on the convergence of preconditioned Krylov subspace methods for the solution of nonsymmetric linear systems is shown. The preconditioners used in this study are diierent variants of incomplete factorizations. It is shown that reorderings for direct methods, such as Reverse Cuthill-McKee and Minimum Degree, can be very beneeci...
متن کاملRiemannian Sparse Coding for Positive Definite Matrices
Inspired by the great success of sparse coding for vector valued data, our goal is to represent symmetric positive definite (SPD) data matrices as sparse linear combinations of atoms from a dictionary, where each atom itself is an SPD matrix. Since SPD matrices follow a non-Euclidean (in fact a Riemannian) geometry, existing sparse coding techniques for Euclidean data cannot be directly extende...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Computational and Applied Mathematics
سال: 2009
ISSN: 0377-0427
DOI: 10.1016/j.cam.2009.01.011