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A separation theorem in property testing

机译:属性测试中的分离定理

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Consider the following seemingly rhetorical question: Is it crucial for a property-tester to know the error parameter epsilon in advance? Previous papers dealing with various testing problems, suggest that the answer may be no, as in these papers there was no loss of generality in assuming that epsilon is given as part of the input, and is not known in advance. Our main result in this paper, however, is that it is possible to separate a natural model of property testing in which epsilon is given as part of the input from the model in which epsilon is known in advance (without making any computational hardness assumptions). To this end, we construct a graph property P, which has the following two properties: (i) There is no tester for P accepting epsilon as part of the input, whose number of queries depends only on epsilon. (ii) For any fixed epsilon, there is a tester for P (that works only for that specific e), which makes a constant number of queries. Interestingly, we manage to construct a separating property T, which is combinatorially natural as it can be expressed in terms of forbidden subgraphs and also computationally natural as it can be shown to belong to coNP. The main tools in this paper are efficiently constructible graphs of high girth and high chromatic number, a result about testing monotone graph properties, as well as basic ideas from the theory of recursive functions. Of independent interest is a precise characterization of the monotone graph properties that can be tested with epsilon being part of the input, which we obtain as one of the main steps of the paper.
机译:考虑以下看似修辞的问题:对于属性测试人员来说,提前知道错误参数epsilon是否至关重要?先前处理各种测试问题的论文认为答案可能不是,因为在这些论文中,假定ε是输入的一部分,并且事先不知道,并没有失去一般性。但是,我们在本文中的主要结果是,可以将自然测试的自然模型(其中输入为ε的一部分)与事先已知的ε模型分开(无需进行任何计算硬度假设)。 。为此,我们构造了一个图形属性P,它具有以下两个属性:(i)没有P接受epsilon作为输入一部分的测试器,其查询次数仅取决于epsilon。 (ii)对于任何固定的epsilon,都有一个用于P的测试器(仅适用于特定的e),它可以进行恒定数量的查询。有趣的是,我们设法构造了一个分离属性T,该属性在组合上是自然的,因为它可以用禁止子图表示,在计算上也是自然的,因为它可以证明属于coNP。本文的主要工具是可有效构造的高围度和高色数图,有关测试单调图特性的结果以及来自递归函数理论的基本思想。独立感兴趣的是可以用epsilon作为输入的一部分来测试单调图特性的精确表征,这是本文的主要步骤之一。

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