最优双源提取器和Ramsey图

Gil Cohen
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引用次数: 19

摘要

本工作的主要贡献是构造了准对数最小熵的双源提取器。也就是说,一个具有最小熵Ο(logn)的两个独立n位源的提取器,它是最优的,直到poly(loglog)因子。构造低熵双源提取器的一个强烈动机是为了构造拉姆齐图。我们的双源提取器很容易在n个顶点上生成(logn)(logloglogn)Ο(1)-Ramsey图。尽管近年来在构造O(logn)-Ramsey图方面取得了令人兴奋的进展,这是本文所做的一系列工作,但目前尚不清楚当前的技术是否可以推动到匹配这个界限。然而,有趣的是,作为当前技术的产物,人们得到了强显式拉姆齐图,即n个顶点上的图,其中连接任何一对顶点的边的存在可以在时间poly(logn)中确定。在我们强烈明确的构造之上,在这项工作中,我们考虑了在poly(n)时间内输出整个图的算法,并在此设置中朝着匹配所需的Ο(logn)界取得进展。我们认为,这是研究拉姆齐图结构的自然环境。这项工作的主要技术新颖之处在于改进了独立保留合并(IPM)的构造,IPM是最近由Cohen和Schulman提出的经过充分研究的合并概念的一种变体。我们的构造是基于与带有建议的相关断续符的新连接。事实上,我们的IPM满足了比原始定义要求的更强、更自然的性质,我们相信它可以找到进一步的应用。
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Towards optimal two-source extractors and Ramsey graphs
The main contribution of this work is a construction of a two-source extractor for quasi-logarithmic min-entropy. That is, an extractor for two independent n-bit sources with min-entropy Ο(logn), which is optimal up to the poly(loglogn) factor. A strong motivation for constructing two-source extractors for low entropy is for Ramsey graphs constructions. Our two-source extractor readily yields a (logn)(logloglogn)Ο(1)-Ramsey graph on n vertices. Although there has been exciting progress towards constructing O(logn)-Ramsey graphs in recent years, a line of work that this paper contributes to, it is not clear if current techniques can be pushed so as to match this bound. Interestingly, however, as an artifact of current techniques, one obtains strongly explicit Ramsey graphs, namely, graphs on n vertices where the existence of an edge connecting any pair of vertices can be determined in time poly(logn). On top of our strongly explicit construction, in this work, we consider algorithms that output the entire graph in poly(n)-time, and make progress towards matching the desired Ο(logn) bound in this setting. In our opinion, this is a natural setting in which Ramsey graphs constructions should be studied. The main technical novelty of this work lies in an improved construction of an independence-preserving merger (IPM), a variant of the well-studied notion of a merger, which was recently introduced by Cohen and Schulman. Our construction is based on a new connection to correlation breakers with advice. In fact, our IPM satisfies a stronger and more natural property than that required by the original definition, and we believe it may find further applications.
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