Communication Complexity of Statistical Distance

Thomas Watson
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引用次数: 7

Abstract

We prove nearly matching upper and lower bounds on the randomized communication complexity of the following problem: Alice and Bob are each given a probability distribution over n elements, and they wish to estimate within ±ε the statistical (total variation) distance between their distributions. For some range of parameters, there is up to a log n factor gap between the upper and lower bounds, and we identify a barrier to using information complexity techniques to improve the lower bound in this case. We also prove a side result that we discovered along the way: the randomized communication complexity of n-bit Majority composed with n-bit Greater Than is Θ (n log n).
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统计距离的通信复杂度
我们证明了以下问题的随机通信复杂度的接近匹配的上界和下界:Alice和Bob分别给定n个元素的概率分布,并且他们希望在±ε内估计其分布之间的统计(总变异)距离。对于某些参数范围,在上界和下界之间存在高达log n个因子的差距,并且我们确定了在这种情况下使用信息复杂性技术来改进下界的障碍。我们还证明了我们在此过程中发现的一个副结果:n-bit Majority与n-bit大于组成的随机通信复杂度为Θ (n log n)。
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