Let a universe of m elements be given, along with a family of subsets of the universe (neighborhoods), each of size at most k. We describe methods for assigning the m elements to points in a small-dimensional vector space (over
Such constructions lead, through a standard correspondence between linear and statistical independence, to the construction of small sample spaces which restrict to the uniform distribution in each neighborhood. (The sample space is a uniformly-weighted family of binary m-vectors).
The size of such a small space will be a function of the number of neighborhoods; and for sparse families, will be substantially smaller than any space which restricts to the uniform distribution in all k-sets. Previous work on small spaces with limited independence focused on providing independence or near-independence in every k-set of the universe.
We show how to construct the sample spaces efficiently both sequentially and in parallel. In case there are polynomially many (in m) neighborhoods, each of size
These spaces provide a new derandomization technique for algorithms; particularly, algorithms related to the Lova´sz local lemma. We also describe applications to the exhaustive testing of
给定一个m个元素的宇宙以及一个宇宙子集(邻域),每个子集的大小最大为k。我们描述了将m个元素分配给小维向量空间(在 通过线性和统计独立性之间的标准对应关系,此类构造导致构造较小的样本空间,从而限制了每个邻域中的均匀分布。 (样本空间是二进制m向量的均匀加权族。) P>
如此小的空间的大小将取决于邻域数量;对于稀疏的家庭,将大大小于所有限制所有k集的均匀分布的空间。先前关于有限独立性的小型空间的研究重点是在宇宙的每个k集中提供独立性或接近独立性。 P>
我们展示了如何有效地顺序和并行构造样本空间。如果存在多项式上的(m个)邻域,每个邻域的大小为 这些空间为算法提供了一种新的去随机化技术;特别是与Lova´sz局部引理有关的算法。我们还将介绍在
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