Convergence of linear algebraic reliability simulation

Seth M. Henry, Christopher H. Griffin, Paul L. Bruhn

Research output: Contribution to journalConference articlepeer-review


In this paper, numerical methods for the solution of a reliability modeling problem are presented by finding the steady state solution of a Markov chain. The reliability modeling problem analyzed is that of a large system made up of two smaller systems each with a varying number of subsystems. The focus of this study is on the optimal choice and formulation of algorithms for the steady-state solution of the generator matrix for the Markov chain associated with the given reliability modeling problem. In this context, iterative linear equation solution algorithms were compared. The Conjugate-Gradient method was determined to have the quickest convergence with the Gauss-Seidel method following close behind for the relevant model structures. Current work associated with this project analyzes the convergence of the Successive Over-Relaxation method. This work is part of a larger program for simulating, processing, and analyzing stochastic processes associated with simulation of naval systems.

Original languageEnglish (US)
Pages (from-to)1-2
Number of pages2
JournalSimulation Series
Issue number6
StatePublished - 2015
EventPoster Session and Student Colloquium Symposium 2015, Part of the 2015 Spring Simulation Multi-Conference, SpringSim 2015 - Alexandria, United States
Duration: Apr 12 2015Apr 15 2015

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications


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