2026-07-22T13:16:52Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:10909 2024-03-06T10:47:10Z ddc:004 open_access

Near-Linear Time Algorithms for Streett Objectives in Graphs and MDPs Chatterjee, Krishnendu Dvořák, Wolfgang Henzinger, Monika Svozil, Alexander model checking graph games Streett games The fundamental model-checking problem, given as input a model and a specification, asks for the algorithmic verification of whether the model satisfies the specification. Two classical models for reactive systems are graphs and Markov decision processes (MDPs). A basic specification formalism in the verification of reactive systems is the strong fairness (aka Streett) objective, where given different types of requests and corresponding grants, the requirement is that for each type, if the request event happens infinitely often, then the corresponding grant event must also happen infinitely often. All omega-regular objectives can be expressed as Streett objectives and hence they are canonical in verification. Consider graphs/MDPs with n vertices, m edges, and a Streett objectives with k pairs, and let b denote the size of the description of the Streett objective for the sets of requests and grants. The current best-known algorithm for the problem requires time O(min(n^2, m sqrt{m log n}) + b log n). In this work we present randomized near-linear time algorithms, with expected running time O~(m + b), where the O~ notation hides poly-log factors. Our randomized algorithms are near-linear in the size of the input, and hence optimal up to poly-log factors. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Krishnendu Chatterjee and Wolfgang Dvořák and Monika Henzinger and Alexander Svozil 2019 Is Part Of LIPIcs, Volume 140, 30th International Conference on Concurrency Theory (CONCUR 2019) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.CONCUR.2019.7 urn:nbn:de:0030-drops-109093 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CONCUR.2019.7 eng https://creativecommons.org/licenses/by/3.0/legalcode