部分测试、通用测试和可分解性

E. Fischer, Yonatan Goldhirsh, Oded Lachish
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引用次数: 20

摘要

对于属性P和子属性P',我们说P是P'-通过q查询部分可测试},如果存在一种算法,可以使用q查询以高概率区分P'中的输入和远离P的输入ε。一些自然属性需要许多查询来测试,但可以划分为少量的子集,这些子集可以用很少的查询进行部分测试,有时甚至是与输入大小无关的数量。对于{0,1}上的性质,可分区的概念与在[14]中独立定义的邻近性的Merlin-Arthur证明(MAPs)密切相关,划分为r个部分可测试性质与证明由r个部分可测试性质之一的恒等式组成的Merlin-Arthur系统相同,给出了一个2向转换为O(log r)大小的证明。我们的主要结果是,对于一些低复杂度的性质,不存在上述划分,而且对于我们的每个性质,甚至不存在一个既具有大尺寸又具有查询效率的部分测试的子性质,特别是改进了[14]中的下界集。为此,我们既没有使用传统的姚式论证,也没有使用最近的通信复杂度方法,而是开辟了一种证明下界的新方法。首先,我们使用熵分析,它允许我们直接将我们的参数应用于双侧检验,从而避免了[14]中从双侧检验到单侧检验的转换成本。一般来说,我们使用假定测试的“区分实例”来表明,子属性的成员的均匀随机选择具有“低熵区域”,最终导致它具有低总熵,因此具有小基集。此外,为了将我们的论点应用于自适应测试,我们使用了一种“重新排列”输入比特的机制(通过一个自适应读取整个输入的决策树)来暴露低熵,否则就不会很明显。我们还探讨了另一个方向上连接的可能性,即一个好的分区(或MAP)的存在是否能够导致查询效率相对较高的标准属性测试。我们提供了一些关于这个问题的初步结果,包括一个可能权衡的简单下界。我们的第二个主要结果是对单侧接近无关测试的限制框架的积极权衡结果。这是通过构建一个“通用测试仪”来实现的,该测试仪对所有允许限制测试的属性都具有相同的工作原理。我们的测试器与Goldreich和Ron在[13]中定义的基于样本的测试(针对非恒定数量的查询)的概念非常相关。特别是,它部分地解决了[13]提出的一个开放性问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Partial tests, universal tests and decomposability
For a property P and a sub-property P', we say that P is P'-partially testable with q queries} if there exists an algorithm that distinguishes, with high probability, inputs in P' from inputs ε-far from P, using q queries. Some natural properties require many queries to test, but can be partitioned into a small number of subsets for which they are partially testable with very few queries, sometimes even a number independent of the input size. For properties over {0,1}, the notion of being thus partitionable ties in closely with Merlin-Arthur proofs of Proximity (MAPs) as defined independently in [14] a partition into r partially-testable properties is the same as a Merlin-Arthur system where the proof consists of the identity of one of the r partially-testable properties, giving a 2-way translation to an O(log r) size proof. Our main result is that for some low complexity properties a partition as above cannot exist, and moreover that for each of our properties there does not exist even a single sub-property featuring both a large size and a query-efficient partial test, in particular improving the lower bound set in [14]. For this we use neither the traditional Yao-type arguments nor the more recent communication complexity method, but open up a new approach for proving lower bounds. First, we use entropy analysis, which allows us to apply our arguments directly to 2-sided tests, thus avoiding the cost of the conversion in [14] from 2-sided to 1-sided tests. Broadly speaking we use "distinguishing instances" of a supposed test to show that a uniformly random choice of a member of the sub-property has "low entropy areas", ultimately leading to it having a low total entropy and hence having a small base set. Additionally, to have our arguments apply to adaptive tests, we use a mechanism of "rearranging" the input bits (through a decision tree that adaptively reads the entire input) to expose the low entropy that would otherwise not be apparent. We also explore the possibility of a connection in the other direction, namely whether the existence of a good partition (or MAP) can lead to a relatively query-efficient standard property test. We provide some preliminary results concerning this question, including a simple lower bound on the possible trade-off. Our second major result is a positive trade-off result for the restricted framework of 1-sided proximity oblivious tests. This is achieved through the construction of a "universal tester" that works the same for all properties admitting the restricted test. Our tester is very related to the notion of sample-based testing (for a non-constant number of queries) as defined by Goldreich and Ron in [13]. In particular it partially resolves an open problem raised by [13].
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