Targeting Clause Type Distributions: a Picklock for Random Satisfiability Problems
The paper introduces the Target-SAT (TSAT) algorithm, which significantly improves the ability to solve random satisfiability problems. This advancement allows for a tripling of tractable problem sizes in challenging scenarios. The authors also discuss the limitations of existing local search algorithms and the critical line of complexity barriers in the context of these problems.
- ▪The TSAT algorithm enhances the solution of NP-complete 3-SAT problems by leveraging statistical information from combinatorial constraints.
- ▪This new approach has led to a substantial increase in the sizes of problems that can be tackled effectively.
- ▪The study identifies a critical parameter line that characterizes particularly hard instances of satisfiability problems.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20328 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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| Summary source text | contentText |
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| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Condensed Matter > Statistical Mechanics arXiv:2605.20328 (cond-mat) [Submitted on 19 May 2026] Title:Targeting Clause Type Distributions: a Picklock for Random Satisfiability Problems Authors:J. Schwardt, J. C. Budich View a PDF of the paper titled Targeting Clause Type Distributions: a Picklock for Random Satisfiability Problems, by J. Schwardt and 1 other authors View PDF HTML (experimental) Abstract:Optimization problems such as the NP-complete 3-SAT provide an important benchmark for the difficult task of finding ground-states in strongly correlated many-body systems with rugged energy landscapes.
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