Domain decomposition algorithms

Tony F. Chan

doi:10.1017/s0962492900002427

Domain decomposition algorithms

2026-11-04

0

PRE

AI

DOI：10.1017/s0962492900002427

原文链接

摘要

En 中文

Domain decomposition refers to divide and conquer techniques for solving partial differential equations by iteratively solving subproblems defined on smaller subdomains. The principal advantages include enhancement of parallelism and localized treatment of complex and irregular geometries, singularities and anomalous regions. Additionally, domain decomposition can sometimes reduce the computational complexity of the underlying solution method.In this article, we survey iterative domain decomposition techniques that have been developed in recent years for solving several kinds of partial differential equations, including elliptic, parabolic, and differential systems such as the Stokes problem and mixed formulations of elliptic problems. We focus on describing the salient features of the algorithms and describe them using easy to understand matrix notation. In the case of elliptic problems, we also provide an introduction to the convergence theory, which requires some knowledge of finite element spaces and elementary functional analysis.

Domain decomposition algorithms

摘要

AI总结

期刊

机构

引用论文