Bengaluru Systems Meetup #11

This is work from my Masters thesis that was accepted to the International Conference on Computer-Aided Verification (CAV) 2025 (conference site). The paper presents a novel symbolic algorithm for the Maximal End Component (MEC) decomposition of a Markov Decision Process (MDP). This is a fundamental operation in Probabilistic Model Checking (which I will introduce in the talk). The key idea behind our algorithm INTERLEAVE is to interleave the computation of Strongly Connected Components (SCCs) with eager elimination of redundant state-action pairs, rather than performing these computations sequentially as done by existing state-of-the-art algorithms. Even though our approach has the same complexity as prior works, an empirical evaluation of INTERLEAVE on the standardized Quantitative Verification Benchmark Set demonstrates that it solves 19 more benchmarks (out of 379) than the closest previous algorithm. On the 149 benchmarks that prior approaches can solve, we demonstrate a 3.81x average speedup in runtime.