A new approximate matrix factorization for implicit time integration in air pollution modeling

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

卷:157

Article

Botchev, MA ; Verwer, JG

关键词: operator splitting;    approximate matrix factorization;    large sparse linear systems;    stiff ODES;    method of lines;    Rosenbrock methods;    air pollution modeling;    GMRES;    Krylov solvers;   

DOI  :  10.1016/S0377-0427(03)00414-X

来源: Elsevier

【 摘 要 】

Implicit time stepping typically requires solution of one or several linear systems with a matrix I - tauJ per time step where J is the Jacobian matrix. If solution of these systems is expensive, replacing I - tauJ with its approximate matrix factorization (AMF) (I - tauR)(I - tauV), R+V=J, often leads to a good compromise between stability and accuracy of the time integration on the one hand and its efficiency on the other hand. For example, in air pollution modeling, AMF has been successfully used in the framework of Rosenbrock schemes. The standard AMF gives an approximation to I - tauJ with the error tau(2) RV, which can be significant in norm. In this paper we propose a new AMF. In assumption that - V is an M-matrix, the error of the new AMF can be shown to have an upper bound tauparallel toRparallel to, while still being asymptotically O(tau(2)). This new AMF, called AMF+, is equal in costs to standard AMF and, as both analysis and numerical experiments reveal, provides a better accuracy. We also report on our experience with another, cheaper AMF and with AMF-preconditioned GMRES. (C) 2003 Elsevier B.V. All rights reserved.

【 授权许可】

Free