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A New Asymptotic Notation: Weak Theta

机译:一个新的渐近符号:弱θ

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摘要

Algorithms represent one of the fundamental issues in computer science, while asymptotic notations are widely accepted as the main tool for estimating the complexity of algorithms. Over the years a certain number of asymptotic notations have been proposed. Each of these notations is based on the comparison of various complexity functions with a given complexity function. In this paper, we define a new asymptotic notation, called "Weak Theta," that uses the comparison of various complexity functions with two given complexity functions. Weak Theta notation is especially useful in characterizing complexity functions whose behaviour is hard to be approximated using a single complexity function. In addition, in order to highlight the main particularities of Weak Theta, we propose and prove several theoretical results: properties of Weak Theta, criteria for comparing two complexity functions, and properties of a new set of complexity functions (also defined in the paper) based on Weak Theta. Furthermore, to illustrate the usefulness of our notation, we discuss an application of Weak Theta in artificial intelligence.
机译:算法代表了计算机科学中的基本问题之一,而渐近符号被广泛接受为估算算法复杂性的主要工具。多年来,已经提出了一定数量的渐近符号。这些符号中的每一个都是基于各种复杂度函数与给定复杂度函数的比较。在本文中,我们定义了一种新的渐近符号,称为“弱Theta”,它使用各种复杂度函数与两个给定的复杂度函数进行比较。弱Theta符号在表征复杂度函数时特别有用,这些复杂度函​​数的行为很难使用单个复杂度函数来近似。此外,为了突出弱Theta的主要特性,我们提出并证明了几个理论结果:弱Theta的性质,比较两个复杂度函数的准则以及一组新的复杂度函数的性质(也在本文中定义)基于弱Theta。此外,为了说明我们的符号的有用性,我们讨论了弱Theta在人工智能中的应用。

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