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arXiv 2207.03361cs.GT
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基于期望竞争比的先知不等式

Prophet Inequalities via the Expected Competitive Ratio

Tomer Ezra, Stefano Leonardi, Rebecca Reiffenhäuser, Matteo Russo, Alexandros Tsigonias-Dimitriadis

AI总结:

本文研究向下封闭约束下的先知不等式,提出用期望比值(EoR)替代传统的期望比(RoE)作为性能指标,建立二者的常数因子约简关系,证明EoR是更强的鲁棒基准,可将已有RoE结果推广到EoR场景。

摘要:

我们研究向下封闭约束下的先知不等式问题。在该问题中,决策者需要对依次到达的元素做出即时且不可撤销的选择,且需满足相关约束。传统上,算法性能会与期望离线最优值进行比较,该指标被称为“期望比(Ratio of Expectations, RoE)”。但RoE存在局限性:它仅能保证相对于最优值的平均性能,在面对事后实现的最优值时可能表现不佳。\n我们研究了一种替代性能指标——期望比值(Expected Ratio, EoR),即算法所得值与先知所得值之比的期望。EoR可提供鲁棒的性能保证:例如,常数级的EoR意味着算法能以常数概率达到离线最优值的常数比例。对于单选择问题的特殊情况,EoR与已被广泛研究的“选中最大值的概率”这一概念完全一致。而对于组合约束问题(也是本文的核心研究对象),EoR是选中最大值概率的自然推广。\n具体而言,我们建立了两个约简关系:对于任意约束,RoE和EoR的差距最多为一个常数因子。此外我们证明,EoR是比RoE更强的基准:对于任意实例(包括约束和分布),RoE至少是EoR的常数比例,但反之不成立。这两个约简关系意味着,在众多已有RoE结果的场景中,都可以得到大量对应的EoR结果。

英文摘要:

We consider prophet inequalities under downward-closed constraints. In this problem, a decision-maker makes immediate and irrevocable choices on arriving elements, subject to constraints. Traditionally, performance is compared to the expected offline optimum, called the \textit{Ratio of Expectations} (RoE). However, RoE has limitations as it only guarantees the average performance compared to the optimum, and might perform poorly against the realized ex-post optimal value. We study an alternative performance measure, the \textit{Expected Ratio} (EoR), namely the expectation of the ratio between algorithm's and prophet's value. EoR offers robust guarantees, e.g., a constant EoR implies achieving a constant fraction of the offline optimum with constant probability. For the special case of single-choice problems the EoR coincides with the well-studied notion of probability of selecting the maximum. However, the EoR naturally generalizes the probability of selecting the maximum for combinatorial constraints, which are the main focus of this paper. Specifically, we establish two reductions: for every constraint, RoE and the EoR are at most a constant factor apart. Additionally, we show that the EoR is a stronger benchmark than the RoE in that, for every instance (constraint and distribution), the RoE is at least a constant fraction of the EoR, but not vice versa. Both these reductions imply a wealth of EoR results in multiple settings where RoE results are known.

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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