2CNF布尔公式可满足性问题与线性空间假设

IF 1.1 3区 计算机科学 Q1 BUSINESS, FINANCE
Tomoyuki Yamakami
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引用次数: 0

摘要

我们的目的是研究由自然大小参数参数化的非确定对数空间(NL)决策、搜索和优化问题的可解性/不可解性,同时使用多项式时间和亚线性空间。我们特别关注2SAT3——2CNF布尔(命题)公式可满足性问题的一个受限变体,其中给定2CNF公式的每个变量最多以文字的形式出现3次——由每个给定布尔公式的变量总数mvbl(ξ)参数化。我们提出了一个新的、实用的工作假设,称为线性空间假设(LSH),它断言(2SAT3,mvbl)不能在多项式时间内在所有实例x上仅使用“次线性”空间(即,常数ε∈[0,1)的mvbl(x)εpolylog(|x|)空间)求解。LSH的直接后果包括L≠NL、LOGDCFL≠LOGCFL和SC≠NSC。在我们的研究中,我们充分利用了“短约简”的一个关键概念,在该概念下,所有参数化多项式时间-次线性空间可解问题的类PsubLIN确实是封闭的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The 2CNF Boolean formula satisfiability problem and the linear space hypothesis

We aim at investigating the solvability/insolvability of nondeterministic logarithmic-space (NL) decision, search, and optimization problems parameterized by natural size parameters using simultaneously polynomial time and sub-linear space. We are particularly focused on 2SAT3—a restricted variant of the 2CNF Boolean (propositional) formula satisfiability problem in which each variable of a given 2CNF formula appears at most 3 times in the form of literals—parameterized by the total number mvbl(ϕ) of variables of each given Boolean formula ϕ. We propose a new, practical working hypothesis, called the linear space hypothesis (LSH), which asserts that (2SAT3,mvbl) cannot be solved in polynomial time using only “sub-linear” space (i.e., mvbl(x)εpolylog(|x|) space for a constant ε[0,1)) on all instances x. Immediate consequences of LSH include LNL, LOGDCFLLOGCFL, and SCNSC. For our investigation, we fully utilize a key notion of “short reductions”, under which the class PsubLIN of all parameterized polynomial-time sub-linear-space solvable problems is indeed closed.

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来源期刊
Journal of Computer and System Sciences
Journal of Computer and System Sciences 工程技术-计算机:理论方法
CiteScore
3.70
自引率
0.00%
发文量
58
审稿时长
68 days
期刊介绍: The Journal of Computer and System Sciences publishes original research papers in computer science and related subjects in system science, with attention to the relevant mathematical theory. Applications-oriented papers may also be accepted and they are expected to contain deep analytic evaluation of the proposed solutions. Research areas include traditional subjects such as: • Theory of algorithms and computability • Formal languages • Automata theory Contemporary subjects such as: • Complexity theory • Algorithmic Complexity • Parallel & distributed computing • Computer networks • Neural networks • Computational learning theory • Database theory & practice • Computer modeling of complex systems • Security and Privacy.
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