以只读分支程序为目标的复杂性测量透视

IF 0.8 4区 计算机科学 Q3 COMPUTER SCIENCE, THEORY & METHODS
Yaqiao Li , Pierre McKenzie
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引用次数: 0

摘要

只读分支程序是一个已知合理下限但仍不完整的计算模型。这里定义并研究了在研究一次读取分支程序中成功的复杂性度量的变体。此外,还揭示了已知边界的一些新的或更简单的证明。对分支程序资源和新度量方法进行了广泛比较。新变体的开发部分是希望解决树评估问题中的读k分支程序。我们还研究了其他计算问题。特别是,对加尔所研究的函数以及博利格和韦格纳所研究的函数的共同看法,引出了阻塞集的一般组合学。我们还获得了具有独立意义的技术组合结果。讨论了取得进一步进展的新线索。此外,还得出了 GEN 函数的非确定读 k 分支程序的指数下限,这与新措施无关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Perspective on complexity measures targeting read-once branching programs
A model of computation for which reasonable yet still incomplete lower bounds are known is the read-once branching program. Here variants of complexity measures successful in the study of read-once branching programs are defined and studied. Some new or simpler proofs of known bounds are uncovered. Branching program resources and the new measures are compared extensively. The new variants are developed in part in the hope of tackling read-k branching programs for the tree evaluation problem. Other computation problems are studied as well. In particular, a common view of a function studied by Gál and a function studied by Bollig and Wegener leads to the general combinatorics of blocking sets. Technical combinatorial results of independent interest are obtained. New leads towards further progress are discussed. An exponential lower bound for non-deterministic read-k branching programs for the GEN function is also derived, independently from the new measures.
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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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