Space characterizations of complexity measures and size-space trade-offs in propositional proof systems

IF 1.1 3区 计算机科学 Q1 BUSINESS, FINANCE
Theodoros Papamakarios , Alexander Razborov
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引用次数: 0

Abstract

We identify two new clusters of proof complexity measures equal up to polynomial and logn factors. The first cluster contains the logarithm of tree-like resolution size, regularized clause and monomial space, and clause space, ordinary and regularized, in regular and tree-like resolution. Consequently, separating clause or monomial space from the logarithm of tree-like resolution size is equivalent to showing strong trade-offs between clause space and length, and equivalent to showing super-critical trade-offs between clause space and depth. The second cluster contains width, Σ2 space (a generalization of clause space to depth 2 Frege systems), ordinary and regularized, and the logarithm of tree-like R(log) size. As an application, we improve a known size-space trade-off for polynomial calculus with resolution. We further show a quadratic lower bound on tree-like resolution size for formulas refutable in clause space 4, and introduce a measure intermediate between depth and the logarithm of tree-like resolution size.

Abstract Image

命题证明系统中复杂性度量和大小-空间权衡的空间表征
我们确定了两个新的证明复杂度测度簇,它们等于多项式和对数⁡n个因素。第一个簇包含树状分辨率大小的对数、正则子句和单项式空间,以及正则和树状分辨率中的正则和正则子句空间。因此,从树状分辨率大小的对数中分离子句或单项式空间,相当于在子句空间和长度之间进行了强有力的权衡,相当于显示了子句空间和深度之间的超临界权衡。第二个簇包含宽度,∑2空间(子句空间到深度2 Frege系统的推广),普通的和正则的,以及树状R的对数(log⁡) 大小作为一个应用,我们改进了具有分辨率的多项式演算的已知大小空间权衡。我们进一步给出了子句空间4中可反驳公式的类树分辨率大小的二次下界,并引入了类树分辨率的深度和对数之间的测度。
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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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