Computable type and computably categorical spaces

IF 1.8 2区 数学 Q1 MATHEMATICS
Journal of Complexity Pub Date : 2026-08-01 Epub Date: 2026-02-03 DOI:10.1016/j.jco.2026.102026
Zvonko Iljazović , Patrik Vasung
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引用次数: 0

Abstract

We examine effective separating sequences on a metric space and, in particular, conditions under which on a metric space every two such sequences are equivalent up to an isometry. Such a metric space is called computably categorical. We prove that an effectively compact metric space (X,d) is computably categorical if the space Iso(X,d) of all isometries of (X,d) has computable type (which in particular holds if Iso(X,d) is a manifold). Using this, we prove that each effectively compact subspace of Euclidean space is computably categorical.
可计算类型空间和可计算分类空间
我们研究了度量空间上的有效分离序列,特别是在度量空间上每两个这样的序列等价于等距的条件。这样的度量空间称为可计算范畴空间。证明了如果(X,d)的所有等距空间Iso(X,d)具有可计算类型(特别是当Iso(X,d)是流形时),则有效紧化度量空间(X,d)是可计算范畴的。由此证明了欧几里得空间的每个有效紧化子空间都是可计算的范畴空间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Complexity
Journal of Complexity 工程技术-计算机:理论方法
CiteScore
3.10
自引率
17.60%
发文量
57
审稿时长
>12 weeks
期刊介绍: The multidisciplinary Journal of Complexity publishes original research papers that contain substantial mathematical results on complexity as broadly conceived. Outstanding review papers will also be published. In the area of computational complexity, the focus is on complexity over the reals, with the emphasis on lower bounds and optimal algorithms. The Journal of Complexity also publishes articles that provide major new algorithms or make important progress on upper bounds. Other models of computation, such as the Turing machine model, are also of interest. Computational complexity results in a wide variety of areas are solicited. Areas Include: • Approximation theory • Biomedical computing • Compressed computing and sensing • Computational finance • Computational number theory • Computational stochastics • Control theory • Cryptography • Design of experiments • Differential equations • Discrete problems • Distributed and parallel computation • High and infinite-dimensional problems • Information-based complexity • Inverse and ill-posed problems • Machine learning • Markov chain Monte Carlo • Monte Carlo and quasi-Monte Carlo • Multivariate integration and approximation • Noisy data • Nonlinear and algebraic equations • Numerical analysis • Operator equations • Optimization • Quantum computing • Scientific computation • Tractability of multivariate problems • Vision and image understanding.
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