论度量空间上的信息序

Oliver Olela Otafudu, Ó. Valero
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引用次数: 0

摘要

信息顺序在计算机科学的数学基础中起着核心作用。具体地说,它们是描述在计算的每一步中信息依次增加的过程的合适工具。为了提供上述过程中信息量的数值量化,S.G. Matthews引入了部分度量和Scott-like拓扑的概念。部分度量的成功主要取决于两个事实。一方面,它们可以归纳出所谓的专门化偏序,它能够在计算机科学中以自然的方式出现的许多空间示例中对现有的顺序结构进行编码。另一方面,当偏度量空间完备时,它们的相关拓扑是scott -类的,因此,它能够以这样一种方式描述上述增加的信息过程,即序列的最优点总是存在并捕获由偏度量测量的信息量;除了可以从序列的成员派生的信息外,它也不包含其他信息。R. Heckmann表明,通过度量和非负实值函数生成偏阶的著名方法的特殊情况下,可以推导出与偏度规相关的偏阶。基于这一事实,我们从信息顺序理论的角度探讨了这一通用方法。具体来说,我们表明这种方法以这样一种方式捕获信息顺序的本质,即所考虑的函数能够量化信息的数量,此外,其测量可用于区分最大元素。此外,我们还证明了这种赋予度量空间偏序的方法也可以应用于偏度量空间,以产生不同于专门化的新偏序。进一步证明了在给定一个完备度量空间和一个非连续函数的情况下,由该一般方法导出的偏序集具有丰富的性质。具体地说,我们不仅证明了它的有序完备性,而且证明了它的定向完备性,此外,我们还证明了由度量导出的拓扑是Scott-like的。因此,这种数学结构可以用于开发基于度量的工具,以模拟计算机科学中增加的信息过程。作为我们的新结果的一个特例,对于一个完全偏度量空间,我们检索了上述关于相关拓扑的Scott-like特征的著名事实,此外,诱导的偏序集是有向完全的,而不仅仅是有序完全的。
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On Information Orders on Metric Spaces
Information orders play a central role in the mathematical foundations of Computer Science. Concretely, they are a suitable tool to describe processes in which the information increases successively in each step of the computation. In order to provide numerical quantifications of the amount of information in the aforementioned processes, S.G. Matthews introduced the notions of partial metric and Scott-like topology. The success of partial metrics is given mainly by two facts. On the one hand, they can induce the so-called specialization partial order, which is able to encode the existing order structure in many examples of spaces that arise in a natural way in Computer Science. On the other hand, their associated topology is Scott-like when the partial metric space is complete and, thus, it is able to describe the aforementioned increasing information processes in such a way that the supremum of the sequence always exists and captures the amount of information, measured by the partial metric; it also contains no information other than that which may be derived from the members of the sequence. R. Heckmann showed that the method to induce the partial order associated with a partial metric could be retrieved as a particular case of a celebrated method for generating partial orders through metrics and non-negative real-valued functions. Motivated by this fact, we explore this general method from an information orders theory viewpoint. Specifically, we show that such a method captures the essence of information orders in such a way that the function under consideration is able to quantify the amount of information and, in addition, its measurement can be used to distinguish maximal elements. Moreover, we show that this method for endowing a metric space with a partial order can also be applied to partial metric spaces in order to generate new partial orders different from the specialization one. Furthermore, we show that given a complete metric space and an inf-continuous function, the partially ordered set induced by this general method enjoys rich properties. Concretely, we will show not only its order-completeness but the directed-completeness and, in addition, that the topology induced by the metric is Scott-like. Therefore, such a mathematical structure could be used for developing metric-based tools for modeling increasing information processes in Computer Science. As a particular case of our new results, we retrieve, for a complete partial metric space, the above-explained celebrated fact about the Scott-like character of the associated topology and, in addition, that the induced partial ordered set is directed-complete and not only order-complete.
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