Monte Carlo Numerical Treatment of Large Linear Algebra Problems

In this paper we deal with performance analysis of Monte Carlo algorithm for large linear algebra problems. We consider applicability and efficiency of the Markov chain Monte Carlo for large problems, i.e., problems involving matrices with a number of non-zero elements ranging between one million and one billion. We are concentrating on analysis of the almost Optimal Monte Carlo (MAO) algorithm for evaluating bilinear forms of matrix powers since they form the so-called Krylov subspaces. Results are presented comparing the performance of the Robust and Non-robust Monte Carlo algorithms. The algorithms are tested on large dense matrices as well as on large unstructured sparse matrices.

• Type: Conference
• Year: 2007
• Where: ICCS
• Authors: Ivan Dimov, Vassil N. Alexandrov, Rumyana Papancheva, Christian Weihrauch
• Keywords: ICCS 2007, Markov Chain Monte Carlo, Monte Carlo, Monte Carlo Algorithms