论文标题
堆栈自动机型的空间复杂性
Space Complexity of Stack Automata Models
论文作者
论文摘要
本文研究了堆栈自动机变体的空间复杂性的几种量度:非搜索堆栈自动机和检查堆栈自动机。这些措施捕获了接受自动机语言(弱度量)的每个单词所需的最小堆栈大小,在任何接受单词(接受度量)上使用的任何接受计算中使用的最大堆栈大小以及任何计算中使用的最大堆栈大小(强度强度)。我们详细介绍了检查堆栈自动机的接受度和强大空间复杂度度量。可能发生三种情况之一:复杂性要么由常数界定,表现为线性函数,要么无法受到输入单词长度的任何函数的界限(并且可以确定发生的情况)。但是,此结果不适合非搜索堆栈自动机。我们提供了一个示例,其中空间复杂性与输入长度的平方根成比例地生长。此外,我们研究接受给定语言的机器的复杂性界限以及空间复杂性属性的可决定性。
This paper examines several measures of space complexity of variants of stack automata: non-erasing stack automata and checking stack automata. These measures capture the minimum stack size required to accept every word in the language of the automaton (weak measure), the maximum stack size used in any accepting computation on any accepted word (accept measure),and the maximum stack size used in any computation (strong measure). We give a detailed characterization of the accept and strong space complexity measures for checking stack automata. Exactly one of three cases can occur: the complexity is either bounded by a constant, behaves like a linear function, or it can not be bounded by any function of the length of the input word (and it is decidable which case occurs). However, this result does not hold for non-erasing stack automata; we provide an example where the space complexity grows proportionally to the square root of the length of the input. Furthermore, we study the complexity bounds of machines which accept a given language, and decidability of space complexity properties.