Low-rank updates of balanced incomplete factorization preconditioners
[EN] Let Ax = b be a large and sparse system of linear equations where A is a nonsingular matrix. An approximate solution is frequently obtained by applying preconditioned terations. Consider the matrix B = A + PQT where P,Q ∈ Rn×k are full rank matrices. In this work, we study the pro...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/107359 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/107359 |
| Access Level: | acceso abierto |
| Palabra clave: | Iterative methods Preconditioning Low rank update Balanced incomplete factorization Sparse linear systems MATEMATICA APLICADA |
| Sumario: | [EN] Let Ax = b be a large and sparse system of linear equations where A is a nonsingular matrix. An approximate solution is frequently obtained by applying preconditioned terations. Consider the matrix B = A + PQT where P,Q ∈ Rn×k are full rank matrices. In this work, we study the problem of updating a previously computed preconditioner for A in order to solve the updated linear system Bx = b by preconditioned iterations. In particular, we propose a method for updating a Balanced Incomplete Factorization preconditioner. The strategy is based on the computation of an approximate Inverse Sherman-Morrison decomposition for an equivalent augmented linear system. Approximation properties of the preconditioned matrix and an analysis of the computational cost of the algorithm are studied. Moreover the results of the numerical experiments with different types of problems show that the proposed method contributes to accelerate the convergence. |
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