NUMERICAL-METHODS IN MARKOV-CHAIN MODELING
成果类型：
Article
署名作者：
PHILIPPE, B; SAAD, Y; STEWART, WJ
署名单位：
University of Minnesota System; University of Minnesota Twin Cities; North Carolina State University
刊物名称：
OPERATIONS RESEARCH
ISSN/ISSBN：
0030-364X
DOI：
10.1287/opre.40.6.1156
发表日期：
1992
页码：
1156-1179
关键词：
摘要：
This paper describes and compares several methods for computing stationary probability distributions of Markov chains. The main linear algebra problem consists of computing an eigenvector of a sparse, nonsymmetric matrix associated with a known eigenvalue. It can also be cast as a problem of solving a homogeneous, singular linear system. We present several methods based on combinations of Krylov subspace techniques, single vector power iteration/relaxation procedures and acceleration techniques. We compare the performance of these methods on some realistic problems.