累积剪枝约束:Max 2-SAT 精确求解的多项式空间新算法
Accumulating Pruning Constraints: New Exact Polynomial-Space Algorithms for Max 2-SAT
AI 导读
论文提出基于累积剪枝约束的确定性多项式空间算法,可在 O*(2^((1-γ)n)) 时间内精确求解多类稠密 Max 2-SAT 实例,其中 γ 为固定正常数。该方法扩展经典 DPLL 分支算法,适用于含大量偏置变量的公式、半随机 2-CNF 公式以及正则扩展图和随机块模型图上的 Max-Cut 问题。
正文 · 原文
Abstract:We present deterministic polynomial-space algorithms that solve Max 2-SAT on $n$ variables exactly in time $\Ostar(2^{(1-\gamma)n})$, for a fixed $\gamma>0$, on several classes of instances whose clause density may grow with $n$. Our main tool extends the classic DPLL branching algorithm. When the search gives a variable its less promising value, comparing the two values of the variable yields a pruning constraint that is satisfied by some optimal assignment. Accumulating these constraints along each branch prunes all but an exponentially small fraction of the assignments. The classes are formulas with many biased variables, semirandom 2-CNF formulas, and Max-Cut on regular expander graphs and on graphs generated by a stochastic block model. In a semirandom formula, the pairs of variables that may share a clause are arbitrary, and the clauses on these pairs are random. For semirandom formulas and block-model graphs, the algorithms are still exact on every input, and the time bound holds with high probability over the input. To our knowledge, these are the first polynomial-space algorithms with such a running time for nontrivial classes of dense Max 2-SAT instances. Beyond these classes, the pruning constraints can be added to any branching algorithm for Max 2-SAT, and we expect them to be of practical relevance for general formulas, in particular for sparse ones, where each constraint involves only a few variables.
| Comments: | Project folder with Lean verification: this https URL |
| Subjects: | Data Structures and Algorithms (cs.DS); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.39992 [cs.DS] |
| (or arXiv:2610.39992v1 [cs.DS] for this version) | |
| https://doi.org/10.48550/arXiv.2610.39992 arXiv-issued DOI via DataCite |
Submission history
From: Konstantin Kutzkov [view email]
[v1]
Thu, 29 Oct 2026 13:12:14 UTC (87 KB)
来源:arXiv cs.AI · arxiv.org