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Sequential Convex Programming for the Efficient Verification of Parametric MDPs

M. Cubuktepe, N. Jansen, S. Junges, J.-P. Katoen, I. Papusha, H. A. Poonawala, and U. Topcu

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.

Citation

M. Cubuktepe, N. Jansen, S. Junges, J.-P. Katoen, I. Papusha, H. A. Poonawala, and U. Topcu. "Sequential Convex Programming for the Efficient Verification of Parametric MDPs," International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS), pp. 133--150, Uppsala, Sweden, April 22--29, 2017.