Auto-Walksat: A Self-Tuning Implementation of Walksat

Auto - Walksat : A Self - Tuning Implementationof

An Efficient Implementation for WalkSAT

Stochastic local search (SLS) algorithms have exhibited great effectiveness in finding models of random instances of the Boolean satisfiability problem (SAT). As one of the most widely known and used SLS algorithm, WalkSAT plays a key role in the evolutions of SLS for SAT, and also hold stateof-the-art performance on random instances. This work proposes a novel implementation for WalkSAT which ...

Parallel WalkSAT with Clause Learning

We present an extension of WalkSAT, a stochastic local search algorithm for solving SAT problems. Our extension learns new clauses by resolving existing clauses based on the current state of a WalkSAT run. We show that clause learning leads to significant speedup in WalkSAT runs, both in terms of fewer steps and faster runtime. We argue that our WalkSAT implementation is easily parallelizable, ...

Using Cost Distributions to Guide Weight Decay in Local Search for SAT

Although clause weighting local search algorithms have produced some of the best results on a range of challenging satisfiability (SAT) benchmarks, this performance is dependent on the careful handtuning of sensitive parameters. When such hand-tuning is not possible, clause weighting algorithms are generally outperformed by self-tuning WalkSAT-based algorithms such as AdaptNovelty and AdaptGWSA...

Backbone Guided Local Search for Maximum Satisfiability

Maximum satisfiability (Max-SAT) is more general and more difficult to solve than satisfiability (SAT). In this paper, we first investigate the effectiveness of Walksat, one of the best local search algorithms designed for SAT, on Max-SAT. We show that Walksat is also effective on Max-SAT, while its effectiveness degrades as the problem is more constrained. We then develop a novel method that e...