🤖 AI Summary
This work resolves a long-standing open problem posed by Alekhnovich and Ben-Sasson: whether WalkSAT exhibits linear expected runtime on random 2-SAT instances in the satisfiable phase (i.e., clause-to-variable ratio ρ < 1). Leveraging a novel synthesis of Markov chain analysis, a carefully constructed potential function, and structural characterizations of the underlying random implication graph, we establish the first rigorous proof that WalkSAT achieves O(n) expected runtime for all ρ < 1—linear in the number n of variables. This result significantly improves upon Papadimitriou’s prior O(n²) upper bound, yielding a tight linear bound. Moreover, it constitutes the first provably linear-runtime analysis of WalkSAT across the entire satisfiable phase, thereby filling a fundamental gap in the theoretical understanding of stochastic local search algorithms for random constraint satisfaction problems.
📝 Abstract
In an influential article Papadimitriou [FOCS 1991] proved that a local search algorithm called WalkSAT finds a satisfying assignment of a satisfiable 2-CNF with $n$ variables in $O(n^2)$ expected time. Variants of the WalkSAT algorithm have become a mainstay of practical SAT solving (e.g., [Hoos and St""utzle 2000]). In the present article we analyse the expected running time of WalkSAT on random 2-SAT instances. Answering a question raised by Alekhnovich and Ben-Sasson [SICOMP 2007], we show that WalkSAT runs in linear expected time for all clause/variable densities up to the random 2-SAT satisfiability threshold.