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arXiv 1108.2613cs.CCcs.FL
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实时的机器识别非正则语言空间复杂度的紧界

Tight bounds for the space complexity of nonregular language recognition by real-time machines

Abuzer Yakaryilmaz, A. C. Cem Say

摘要:

我们研究了实时的【形】最小内存量,而不是单向的,计算接受非正则语言。我们认为确定性,不确定性和交替机器工作在强,中,弱空间,并处理一般或一元输入。在大多数情况下,我们能够证明单向机的下界在实时的【形】情况下仍然是紧的。其他设备上的非定期接受内存下限也得到解决。结果表明,增加实时的【形】下推自动机的堆栈数量可以导致非正则语言识别的空间使用总量的指数级改善。

英文摘要:

We examine the minimum amount of memory for real-time, as opposed to one-way, computation accepting nonregular languages. We consider deterministic, nondeterministic and alternating machines working within strong, middle and weak space, and processing general or unary inputs. In most cases, we are able to show that the lower bounds for one-way machines remain tight in the real-time case. Memory lower bounds for nonregular acceptance on other devices are also addressed. It is shown that increasing the number of stacks of real-time pushdown automata can result in exponential improvement in the total amount of space usage for nonregular language recognition.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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