用局部块法求解参数稀疏线性系统,2

Tateaki Sasaki, D. Inaba, F. Kako
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引用次数: 1

摘要

本文作者Inaba和Kako在最近的一篇论文[6]中提出了局部块算法,用于求解工业中出现的参数稀疏线性系统,使得到的解适合于确定最优参数值。他们采用了图论的处理方法,其方法的要点是选择满足若干限制条件的强连通子图,形成所谓的“特征系统”。然而,选择子图的方法很复杂,似乎不适合大型系统。本文假设用户指定了特征系统的少量代表性顶点,给出了一种寻找特征系统的简单方法。然后,我们给出了将给定图分解为强连通子图的一种简单而令人满意的方法。该方法采用了强连通分量(SCC)分解算法。新方法的复杂度为0(#(顶点)+#(边))。我们通过人工制作的三个100个顶点的图成功地测试了我们的方法,这些图显示了不同但典型的特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Solving Parametric Sparse Linear Systems by Local Blocking, II
The present author, Inaba and Kako proposed local blocking in a recent paper [6], for solving parametric sparse linear systems appearing in industry, so that the obtained solution is suited for determining optimal parameter values. They employed a graph theoretical treatment, and the points of their method are to select strongly connected sub graphs satisfying several restrictions and to form the so-called "characteristic system". The method of selecting sub graphs is, however, complicated and seems to be unsuited for big systems. In this paper, assuming that a small number of representative vertices of the characteristic system are specified by the user, we give a simple method of finding a characteristic system. Then, we present a simple and satisfactory method of decomposing the given graph into strongly connected sub graphs. The method applies the SCC (strongly connected component) decomposition algorithm. The complexity of new method is O(# (vertex) +# (edge)). We test our method successfully by three graphs of 100 vertices made artificially showing different but typical features.
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