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Finding Steady States of Communicating Markov Processes Combining Aggregation/Disaggregation with Tensor Techniques

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Computer Performance Engineering (EPEW 2016)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9951))

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Abstract

Stochastic models for interacting processes feature a dimensionality that grows exponentially with the number of processes. This state space explosion severely impairs the use of standard methods for the numerical analysis of such Markov chains. In this work, we develop algorithms for the approximation of steady states of structured Markov chains that consider tensor train decompositions, combined with well-established techniques for this problem – aggregation/disaggregation techniques. Numerical experiments demonstrate that the newly proposed algorithms are efficient on the determination of the steady state of a representative set of models.

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Acknowledgments

I thank Daniel Kressner (EPF Lausanne) for helpful discussions.

Author information

Authors and Affiliations

  1. EPF Lausanne, SB-MATHICSE-ANCHP, Station 8, CH-1015, Lausanne, Switzerland

    Francisco Macedo

  2. IST, Alameda Campus, Av. Rovisco Pais, 1, 1049-001, Lisbon, Portugal

    Francisco Macedo

Authors
  1. Francisco Macedo
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Correspondence to Francisco Macedo .

Editor information

Editors and Affiliations

  1. Ghent University, Gent, Belgium

    Dieter Fiems

  2. University of Florence, Firenze, Italy

    Marco Paolieri

  3. University of the Aegean, Chios, Greece

    Agapios N. Platis

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Macedo, F. (2016). Finding Steady States of Communicating Markov Processes Combining Aggregation/Disaggregation with Tensor Techniques. In: Fiems, D., Paolieri, M., Platis, A. (eds) Computer Performance Engineering. EPEW 2016. Lecture Notes in Computer Science(), vol 9951. Springer, Cham. https://doi.org/10.1007/978-3-319-46433-6_4

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  • DOI: https://doi.org/10.1007/978-3-319-46433-6_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46432-9

  • Online ISBN: 978-3-319-46433-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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