加权自动机和逻辑满足计算复杂性

IF 0.8 4区 计算机科学 Q3 COMPUTER SCIENCE, THEORY & METHODS
Peter Kostolányi
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引用次数: 0

摘要

复杂性类,如 #P、⊕P、GapP、OptP、NPMV,或由多项式时间模糊非决定性图灵机实现的模糊语言类,都可以用一个合适语序 S 的类 NP[S] 来描述,这个类是通过 S 上的加权图灵机定义的,就像 NP 在非加权设置中的定义一样。其他复杂度类也可以提升到定量世界,由此产生的类与原始类的关系就像加权自动机或逻辑与其非加权对应物的关系一样。文章调查了现有文献中隐含的这些鲜为人知的关联,并提出了研究加权复杂度类的系统方法。文章证明,当 S 是有限生成时,SAT 对加权命题逻辑的扩展对于 NP[S] 是完整的。此外,还为每种配线 S 引入了一个类 FP[S] 作为 P 的对应物,并考虑了 FP[S] 和 NP[S] 之间的关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Weighted automata and logics meet computational complexity

Complexity classes such as #P, P, GapP, OptP, NPMV, or the class of fuzzy languages realised by polynomial-time fuzzy nondeterministic Turing machines, can all be described in terms of a class NP[S] for a suitable semiring S, defined via weighted Turing machines over S similarly as NP is defined in the unweighted setting. Other complexity classes can be lifted to the quantitative world as well, the resulting classes relating to the original ones in the same way as weighted automata or logics relate to their unweighted counterparts. The article surveys these too-little-known connexions implicit in the existing literature and suggests a systematic approach to the study of weighted complexity classes. An extension of SAT to weighted propositional logic is proved to be complete for NP[S] when S is finitely generated. Moreover, a class FP[S] is introduced for each semiring S as a counterpart to P, and the relations between FP[S] and NP[S] are considered.

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来源期刊
Information and Computation
Information and Computation 工程技术-计算机:理论方法
CiteScore
2.30
自引率
0.00%
发文量
119
审稿时长
140 days
期刊介绍: Information and Computation welcomes original papers in all areas of theoretical computer science and computational applications of information theory. Survey articles of exceptional quality will also be considered. Particularly welcome are papers contributing new results in active theoretical areas such as -Biological computation and computational biology- Computational complexity- Computer theorem-proving- Concurrency and distributed process theory- Cryptographic theory- Data base theory- Decision problems in logic- Design and analysis of algorithms- Discrete optimization and mathematical programming- Inductive inference and learning theory- Logic & constraint programming- Program verification & model checking- Probabilistic & Quantum computation- Semantics of programming languages- Symbolic computation, lambda calculus, and rewriting systems- Types and typechecking
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