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The Exponential-Dual Matrix Method: Applications to Markov Chain Analysis

Abstract : Classic performance evaluation using queueing theory is usually done assuming a stable model in equilibrium. However, there are situations where we are interested in the transient phase. In this case, the main metrics are built around the model's state distribution at an arbitrary point in time. In dependability, a significant part of the analysis is done in the transient phase. In previous work, we developed an approach to derive distributions of some continuous time Markovian models, built around uniformization (also called Jensen's method), transforming the problem into a discrete time one, and the concept of stochastic duality. This combination of tools provides significant simplifications in many cases. However, stochastic duality does not always exist. Recently, we discovered that an idea of algebraic duality, formally similar to stochastic duality, can be defined and applied to any linear differential system (or equivalently, to any matrix). In this case, there is no limitation, the transformation is always possible. We call it the exponential-dual matrix method. In the article, we describe the limitations of stochastic duality and how the exponential-dual matrix method operates for any system, stochastic or not. These concepts are illustrated throughout our article with specific examples, including the case of infinite matrices.
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https://hal.inria.fr/hal-03260400
Contributor : Gerardo Rubino Connect in order to contact the contributor
Submitted on : Monday, June 14, 2021 - 9:58:39 PM
Last modification on : Monday, April 4, 2022 - 9:28:24 AM
Long-term archiving on: : Thursday, September 16, 2021 - 8:26:31 AM

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  • HAL Id : hal-03260400, version 1

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Gerardo Rubino, Alan Krinik. The Exponential-Dual Matrix Method: Applications to Markov Chain Analysis. R. Swift, A. Krinik, J. Switkes and J. Park. Stochastic Processes and Functional Analysis: New Perspectives, 774, pp.1-23, 2021, Contemporary Mathematics. ⟨hal-03260400⟩

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