A Study of Parallel Continuous Local Search
This paper studies parallel Continuous Local Search (CLS) on Boolean satisfiability problems with symmetric pseudo-Boolean constraints. By relaxing the problem to continuous optimization on a hypercube, experiments reveal that redundant constraints inhibit convergence; CLS can quickly complete partial assignments in hybrid settings; and local search rapidly converges to a stable solution quality distribution due to saddle-dense objectives. These findings inform practical uses of CLS for SAT on modern accelerator hardware.
[2606.06656] A Study of Parallel Continuous Local Search
[Submitted on 4 Jun 2026]
Title:A Study of Parallel Continuous Local Search
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Abstract:We study parallel Continuous Local Search (CLS) as a solution approach for Boolean satisfiability problems with symmetric pseudo-Boolean (PB) constraints. Here, the $n$-variable PB-satisfiability problem is relaxed to a continuous optimisation problem with a differentiable objective function on an $n$-dimensional hypercube. For satisfiable instances, the global minimisers of this optimisation problem correspond to satisfying assignments of the SAT problem at hand. We present several novel findings via empirical experiments: (i) redundant constraints can inhibit rather than accelerate convergence; (ii) CLS shows promise as a sub-solver in hybridised settings, quickly completing partial assignments; and (iii) local search rapidly converges to a stable distribution of solution quality (i.e., degree of satisfaction), due to saddle-dense objectives where additional solver steps yield diminishing returns. Our findings inform practical uses of CLS for SAT on modern accelerator hardware.
Subjects:
Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
ACM classes: G.1.6; F.2.2; I.2.8
Cite as: arXiv:2606.06656 [cs.AI]
(or arXiv:2606.06656v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.06656
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Cody Christopher PhD [view email] [v1] Thu, 4 Jun 2026 19:03:32 UTC (3,912 KB)
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