Polynomial Probabilistic Invariants and the Optional Stopping Theorem

by   Anne Schreuder, et al.

In this paper we present methods for the synthesis of polynomial invariants for probabilistic transition systems. Our approach is based on martingale theory. We construct invariants in the form of polynomials over program variables, which give rise to martingales. These polynomials are program invariants in the sense that their expected value upon termination is the same as their value at the start of the computation. In order to guarantee this we apply the Optional Stopping Theorem. Concretely, we present two approaches. The first is restricted to linear systems. In this case under positive almost sure termination there is a reduction to finding linear invariants for deterministic transition systems. Secondly, by exploiting geometric persistence properties we construct martingale invariants for general polynomial transition system. We have implemented this approach and it works on our examples.


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