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arXiv · 1702.00063

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
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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 pro- grams are model repair and parameter synthesis. We demonstrate the scalability and quality of our approach by well-known benchmarks

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Murat Cubuktepe, Nils Jansen, Sebastian Junges, Joost-Pieter Katoen, Ivan Papusha, Hasan A. Poonawala, Ufuk Topcu. 2017-01-26. Sequential Convex Programming for the Efficient Verification of Parametric MDPs. https://arxiv.org/abs/1702.00063

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