On a Conjecture of Feige for Discrete Log-Concave Distributions

IF 0.9 3区 数学 Q2 MATHEMATICS SIAM Journal on Discrete Mathematics Pub Date : 2024-01-05 DOI:10.1137/22m1539514
Abdulmajeed Alqasem, Heshan Aravinda, Arnaud Marsiglietti, James Melbourne
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Abstract

SIAM Journal on Discrete Mathematics, Volume 38, Issue 1, Page 93-102, March 2024.
Abstract. A remarkable conjecture of Feige [SIAM J. Comput., 35 (2006), pp. 964–984] asserts that for any collection of [math] independent nonnegative random variables [math], each with expectation at most 1, [math], where [math]. In this paper, we investigate this conjecture for the class of discrete log-concave probability distributions, and we prove a strengthened version. More specifically, we show that the conjectured bound [math] holds when [math]’s are independent discrete log-concave with arbitrary expectation.
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关于离散对数凹分布的费格猜想
SIAM 离散数学杂志》,第 38 卷,第 1 期,第 93-102 页,2024 年 3 月。 摘要。费格的一个惊人猜想 [SIAM J. Comput., 35 (2006), pp.在本文中,我们针对离散对数凹概率分布类研究了这一猜想,并证明了它的加强版。更具体地说,我们证明了当[math]的独立离散对数凹具有任意期望时,猜想的约束[math]成立。
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来源期刊
CiteScore
1.90
自引率
0.00%
发文量
124
审稿时长
4-8 weeks
期刊介绍: SIAM Journal on Discrete Mathematics (SIDMA) publishes research papers of exceptional quality in pure and applied discrete mathematics, broadly interpreted. The journal''s focus is primarily theoretical rather than empirical, but the editors welcome papers that evolve from or have potential application to real-world problems. Submissions must be clearly written and make a significant contribution. Topics include but are not limited to: properties of and extremal problems for discrete structures combinatorial optimization, including approximation algorithms algebraic and enumerative combinatorics coding and information theory additive, analytic combinatorics and number theory combinatorial matrix theory and spectral graph theory design and analysis of algorithms for discrete structures discrete problems in computational complexity discrete and computational geometry discrete methods in computational biology, and bioinformatics probabilistic methods and randomized algorithms.
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