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A comparative analysis of the successive lumping and the lattice path counting algorithms

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TLDR
This paper provides a comparison of the successive lumping (SL) methodology developed in Katehakis et al. (2015) with the popular lattice path counting (Mohanty (1979)) in obtaining rate matrices for queueing models, satisfying the specific quasi birth and death structure as in Van Leeuwaarden et al (2009).
Abstract
This paper provides a comparison of the successive lumping (SL) methodology developed in Katehakis et al. (2015) with the popular lattice path counting (Mohanty (1979)) in obtaining rate matrices for queueing models, satisfying the specific quasi birth and death structure as in Van Leeuwaarden et al. (2009) and Van Leeuwaarden and Winands (2006). The two methodologies are compared both in terms of applicability requirements and numerical complexity by analyzing their performance for the same classical queueing models considered in Van Leeuwaarden et al. (2009). The main findings are threefold. First, when both methods are applicable, the SL-based algorithms outperform the lattice path counting algorithm (LPCA). Second, there are important classes of problems (for example, models with (level) nonhomogenous rates or with finite state spaces) for which the SL methodology is applicable and for which the LPCA cannot be used. Third, another main advantage of SL algorithms over lattice path counting is that the former includes a method to compute the steady state distribution using this rate matrix.

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Citations
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Journal ArticleDOI

Inversion and spectral analysis of matrices arising in the analysis of Markov processes

TL;DR: In this paper, a fast and exact inverse algorithm for nearly tridiagonal matrices arising in the analysis of Markov processes is presented. But the algorithm is not suitable for counting matrices.
Journal ArticleDOI

Parallel computing for Markov chains with islands and ports

TL;DR: An algorithm to calculate invariant distributions of large Markov chains whose state spaces are partitioned into “islands” and “ports” is developed in the framework of the “state reduction approach”, but the special structure of the state space allows calculation of the invariant distribution to be done in parallel.
References
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MonographDOI

Introduction to matrix analytic methods in stochastic modeling

TL;DR: This chapter discusses quasi-Birth-and-Death Processes, a large number of which are based on the Markovian Point Processes and the Matrix-Geometric Distribution, as well as algorithms for the Rate Matrix.
Book

Principles of random walk

Frank Spitzer
TL;DR: In this article, a very special class of random processes, namely to random walk on the lattice points of ordinary Euclidean space, is studied, and the author considered this high degree of specialization worth while because of the theory of such random walks is far more complete than that of any larger class of Markov chains.
Proceedings ArticleDOI

Multiplying matrices faster than coppersmith-winograd

TL;DR: An automated approach for designing matrix multiplication algorithms based on constructions similar to the Coppersmith-Winograd construction is developed and a new improved bound on the matrix multiplication exponent ω<2.3727 is obtained.
Journal ArticleDOI

Updating the inverse of a matrix

William W. Hager
- 01 Jun 1989 - 
TL;DR: The history of these fomulas is presented and various applications to statistics, networks, structural analysis, asymptotic analysis, optimization, and partial differential equations are discussed.
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