Reversible Pebble Games and the Relation Between Tree-Like and General Resolution Space

IF 0.7 3区 计算机科学 Q3 COMPUTER SCIENCE, THEORY & METHODS
J. Torán, Florian Wörz
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引用次数: 2

Abstract

We show a new connection between the clause space measure in tree-like resolution and the reversible pebble game on graphs. Using this connection, we provide several formula classes for which there is a logarithmic factor separation between the clause space complexity measure in tree-like and general resolution. We also provide upper bounds for tree-like resolution clause space in terms of general resolution clause and variable space. In particular, we show that for any formula F , its tree-like resolution clause space is upper bounded by space $$(\pi)$$ ( π ) $$(\log({\rm time}(\pi))$$ ( log ( time ( π ) ) , where $$\pi$$ π is any general resolution refutation of F . This holds considering as space $$(\pi)$$ ( π ) the clause space of the refutation as well as considering its variable space. For the concrete case of Tseitin formulas, we are able to improve this bound to the optimal bound space $$(\pi)\log n$$ ( π ) log n , where n is the number of vertices of the corresponding graph
可逆卵石博弈及树状空间与一般分辨率空间的关系
我们展示了树状解析中的子句空间测度与图上的可逆卵石对策之间的新联系。利用这种联系,我们提供了几个公式类,在树状和一般分辨率中,子句空间复杂性测度之间存在对数因子分离。我们还根据一般解析子句和变量空间给出了树状解析子句空间的上界。特别地,我们证明了对于任何公式F,其树状解析子句空间是由空间$$(\pi)$$(π)$$。考虑反驳的子句空间$$(\pi)$$(π)为空间,并考虑其变量空间。对于Tseitin公式的具体情况,我们能够将该界改进到最优界空间$$(\pi)\logn$$(π)logn,其中n是对应图的顶点数
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来源期刊
Computational Complexity
Computational Complexity 数学-计算机:理论方法
CiteScore
1.50
自引率
0.00%
发文量
16
审稿时长
>12 weeks
期刊介绍: computational complexity presents outstanding research in computational complexity. Its subject is at the interface between mathematics and theoretical computer science, with a clear mathematical profile and strictly mathematical format. The central topics are: Models of computation, complexity bounds (with particular emphasis on lower bounds), complexity classes, trade-off results for sequential and parallel computation for "general" (Boolean) and "structured" computation (e.g. decision trees, arithmetic circuits) for deterministic, probabilistic, and nondeterministic computation worst case and average case Specific areas of concentration include: Structure of complexity classes (reductions, relativization questions, degrees, derandomization) Algebraic complexity (bilinear complexity, computations for polynomials, groups, algebras, and representations) Interactive proofs, pseudorandom generation, and randomness extraction Complexity issues in: crytography learning theory number theory logic (complexity of logical theories, cost of decision procedures) combinatorial optimization and approximate Solutions distributed computing property testing.
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