一个基于可数清醒空间的二分结果

IF 0.8 4区 计算机科学 Q3 COMPUTER SCIENCE, THEORY & METHODS
Hualin Miao , Qingguo Li
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引用次数: 0

摘要

笛卡尔闭范畴在为高阶程序设计语言提供指称语义方面起着重要作用。在本文中,我们试图识别基于点可数清醒空间的CS⊥范畴的笛卡尔闭合子范畴,并且我们提出了一个结论,称为CS⊥范畴的二分结果。这个结果解释了CS⊥的任何笛卡尔闭合满子范畴要么包含在弱紧开连通空间的范畴内,要么包含在主连通空间的范畴内。为了证明二分定理,我们首先推导出,如果从X到X的所有连续函数的空间是局部紧的,则每个点可数的清醒空间X是局部连通的。接下来,我们证明了CS⊥中的函数空间是局部连通的,只有当它的输入空间是弱紧开连通的,或者它的输出空间是主要连通的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A dichotomy result for countably based sober spaces
Cartesian closed categories have been playing fundamental roles in providing denotational semantic for higher-order programming languages. In this paper we try to identify Cartesian closed subcategories of the category CS of pointed countably based sober spaces, and we present a conclusion known as the dichotomy result in the category CS. This result explains that any Cartesian closed full subcategory of CS is contained within either the category of weakly compact open connected spaces or that of principally connected spaces.
To prove our dichotomy theorem, we first deduce that every pointed countably based sober space X is locally connected, if the space of all continuous functions from X to X is locally compact. Next, we demonstrate that a function space in CS is locally connected only if its input space is either weakly compact open connected or its output space is principally connected.
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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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