arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2101.04036math.PRcs.DMmath.CO
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随机矩形矩阵的离散性

The Discrepancy of Random Rectangular Matrices

Dylan J. Altschuler, Jonathan Niles-Weed

摘要:

贝克-菲亚拉猜想是组合数学中的一个基本问题,最近的一种方法是理解随机整数矩阵何时具有常数差异。我们给出了一个完整的答案,这个问题的两个自然模型:矩阵与伯努利或泊松条目。对于泊松矩阵,我们进一步描述了任何矩形长宽比的差异。这些结果为Hoberg和Rothvoss(SODA 2019)以及Franks和Saks(Random Structures Algorithms 2020)的问题提供了尖锐的答案。我们的主要工具是一个条件二阶矩方法结合斯坦的方法交换对。虽然以前的方法仅限于稠密矩阵,但我们的技术允许我们处理所有密度的矩阵。这可能是其他稀疏随机约束满足问题的独立利益。

英文摘要:

A recent approach to the Beck-Fiala conjecture, a fundamental problem in combinatorics, has been to understand when random integer matrices have constant discrepancy. We give a complete answer to this question for two natural models: matrices with Bernoulli or Poisson entries. For Poisson matrices, we further characterize the discrepancy for any rectangular aspect ratio. These results give sharp answers to questions of Hoberg and Rothvoss (SODA 2019) and Franks and Saks (Random Structures Algorithms 2020). Our main tool is a conditional second moment method combined with Stein's method of exchangeable pairs. While previous approaches are limited to dense matrices, our techniques allow us to work with matrices of all densities. This may be of independent interest for other sparse random constraint satisfaction problems.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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