Improved Bounds for Sampling Solutions of Random CNF Formulas

被引：0

作者：

He, Kun ^{[1
]}

Wu, Kewen ^{[2
]}

Yang, Kuan ^{[3
]}

机构：

[1] Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China

[2] Univ Calif Berkeley, Berkeley, CA 94720 USA

[3] Shanghai Jiao Tong Univ, John Hoperoft Ctr Comp Sci, Shanghai, Peoples R China

来源：

PROCEEDINGS OF THE 2023 ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, SODA | 2023年

基金：

中国国家自然科学基金;

关键词：

WALKSAT;

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Let Phi be a random k-CNF formula on n variables and m clauses, where each clause is a disjunction of k literals chosen independently and uniformly. Our goal is, for most Phi, to (approximately) uniformly sample from its solution space. Let alpha = m/n be the density. The previous best algorithm runs in time n(poly(k,alpha)) for any alpha less than or similar to 2(k/300) [Galanis, Goldberg, Guo, and Yang, SIAM J. Comput.'21]. In contrast, our algorithm runs in almost-linear time for any alpha less than or similar to 2(k/3).

引用

页码：3330 / 3361

页数：32

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