2001 International Symposium on Performance Evaluation of Computer and
Telecommunication Systems, 15-19 July 2001

TITLE: Iterative Aggregation-Disaggregation versus Block Gauss-Seidel on
Stochastic Automata Networks with Unfavorable Partitionings

AUTHORS: Oleg Gusak and Tugrul Dayar

ABSTRACT: This paper investigates the performance of iterative 
aggregation-disaggregation (IAD) on continuous-time stochastic automata 
networks (SANs) having relatively large blocks in lumpable partitionings. 
To overcome difficulties associated with solving large diagonal blocks at
each iteration of the particular IAD algorithm, the recursive implementation
of block Gauss-Seidel (BGS) for SANs introduced in [1] is employed. The
performance of IAD is compared with BGS. The results of experiments show 
that it is possible to tune IAD so that it outperforms BGS.

KEYWORDS: Stochastic automata networks; Lumpability; Iterative 
aggregation-disaggregation; Block Gauss-Seidel