Sequential Convex Programming for the Efficient Verification of Parametric MDPs

  • Murat Cubuktepe
  • Nils Jansen
  • Sebastian Junges
  • Joost-Pieter Katoen
  • Ivan Papusha
  • Hasan A. Poonawala
  • Ufuk Topcu
Conference paper

DOI: 10.1007/978-3-662-54580-5_8

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10206)
Cite this paper as:
Cubuktepe M. et al. (2017) Sequential Convex Programming for the Efficient Verification of Parametric MDPs. In: Legay A., Margaria T. (eds) Tools and Algorithms for the Construction and Analysis of Systems. TACAS 2017. Lecture Notes in Computer Science, vol 10206. Springer, Berlin, Heidelberg

Abstract

Multi-objective verification problems of parametric Markov decision processes under optimality criteria can be naturally expressed as nonlinear programs. We observe that many of these computationally demanding problems belong to the subclass of signomial programs. This insight allows for a sequential optimization algorithm to efficiently compute sound but possibly suboptimal solutions. Each stage of this algorithm solves a geometric programming problem. These geometric programs are obtained by convexifying the nonconvex constraints of the original problem. Direct applications of the encodings as nonlinear programs are model repair and parameter synthesis. We demonstrate the scalability and quality of our approach by well-known benchmarks.

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Murat Cubuktepe
    • 1
  • Nils Jansen
    • 1
  • Sebastian Junges
    • 2
  • Joost-Pieter Katoen
    • 2
  • Ivan Papusha
    • 1
  • Hasan A. Poonawala
    • 1
  • Ufuk Topcu
    • 1
  1. 1.The University of Texas at AustinAustinUSA
  2. 2.RWTH Aachen UniversityAachenGermany

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