Improved Strongly Polynomial Algorithms for Deterministic MDPs, 2VPI Feasibility, and Discounted All-Pairs Shortest Paths

2021/10/28 by Karczmarz, Adam · 1 citation
#Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.2110.15070

Abstract

We revisit the problem of finding optimal strategies for deterministic Markov Decision Processes (DMDPs), and a closely related problem of testing feasibility of systems of m linear inequalities on n real variables with at most two variables per inequality (2VPI). We give a randomized trade-off algorithm solving both problems and running in O(nmh+(n/h)³) time using O(n²/h+m) space for any parameter h∈ [1,n]. In particular, using subquadratic space we get O(nm+n^3/2m^3/4) running time, which improves by a polynomial factor upon all the known upper bounds for non-dense instances with m=O(n^2-ε). Moreover, using linear space we match the randomized O(nm+n³) time bound of Cohen and Megiddo [SICOMP'94] that required Θ(n²+m) space. Additionally, we show a new algorithm for the Discounted All-Pairs Shortest Paths problem, introduced by Madani et al. [TALG'10], that extends the DMDPs with optional end vertices. For the case of uniform discount factors, we give a deterministic algorithm running in O(n^3/2m^3/4) time, which improves significantly upon the randomized bound O(n²√(m)) of Madani et al.

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