On the Metric-based Approximate Minimization of Markov Chains

TL;DR

This paper investigates the problem of approximating Markov Chains with fewer states using a metric-based approach, providing theoretical insights and practical algorithms for optimal and suboptimal solutions.

Contribution

It introduces a bilinear program characterization for the approximation problem and proposes an expectation-maximization inspired method for practical solutions.

Findings

Optimal approximations always exist.

The problem's threshold is in PSPACE and NP-hard.

The proposed method outperforms bilinear solvers in experiments.

Abstract

In this paper, we address the approximate minimization problem of Markov Chains (MCs) from a behavioral metric-based perspective. Specifically, given a finite MC and a positive integer k, we are looking for an MC with at most k states having minimal distance to the original. The metric considered in this work is the bisimilarity distance of Desharnais et al.. For this metric we show that (1) optimal approximations always exist; (2) the problem has a bilinear program characterization; and (3) prove that its threshold problem is in PSPACE and NP-hard. In addition to the bilinear program solution, we present an approach inspired by expectation maximization techniques for computing suboptimal solutions to the problem. Experiments suggest that our method gives a practical approach that outperforms the bilinear program implementation run on state-of-the-art bilinear solvers.