arXiv:2606.06656v1 Announce Type: new 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 constrai
The continuous evolution of computational methods for complex problems drives research into more efficient solving mechanisms.
Improved parallel continuous local search could significantly accelerate the solution of hard Boolean satisfiability problems, impacting verification, AI planning, and optimization.
This research could lead to more robust and scalable algorithms for a class of combinatorial problems, enhancing the capabilities of systems reliant on satisfiability solvers.
- · AI algorithm developers
- · Optimization software companies
- · Researchers in formal verification
More efficient general-purpose solvers emerge for complex computational problems.
This efficiency could enable faster development and deployment of more sophisticated AI systems and software.
Reduced time and cost in problem-solving could accelerate innovation across various scientific and engineering domains.
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Read at arXiv cs.AI