Prophet inequalities consist of many beautiful statements that establish tight performance ratios between online and offline allocation algorithms. Typically, tightness is established by constructing an algorithmic guarantee and a worst-case instance separately, whose bounds match as a result of some "ingenuity". In this paper, we instead formulate the construction of the worst-case instance as an optimization problem, which directly finds the tight ratio without needing to construct two bounds separately. Our analysis of this complex optimization problem involves identifying structure in a new "Type Coverage" dual problem. It can be seen as akin to the celebrated Magician and OCRS (Online Contention Resolution Scheme) problems, except more general in that it can also provide tight ratios relative to the optimal offline allocation, whereas the earlier problems only establish tight ratios relative to the ex-ante relaxation of the offline problem. Through this analysis, our paper provides a unified framework that derives new results and recovers many existing ones. First, we show that the "oblivious" method of setting a static threshold due to Chawla et al. (2020) is, surprisingly, best-possible among all static threshold algorithms, for any number $k$ of starting units. We emphasize that this result is derived without needing to explicitly find any counterexample instances. We establish similar "no separation" results for static thresholds in the IID setting, which although previously known, required the construction of complicated counterexamples. Finally, our framework and in particular our Type Coverage problem yields a simplified derivation of the tight 0.745 ratio when $k=1$ in the IID setting.
翻译:先知的不平等包括许多美丽的声明, 从而在在线和离线分配算法之间建立严格的绩效比率。 通常, 建立一种算法保证和最坏案例( 最坏案例方案), 其界限与某些“ 宽度” 相匹配。 在本文中, 我们将最坏案例的构建发展成一个优化问题, 直接发现最紧比例, 而不需要分别构建两个界限。 我们对这一复杂的优化问题的分析涉及在一个新的“ 覆盖” 双重问题中找出结构。 它可以被视为类似于著名的魔术师和 ORS( 在线内容解析方案) 的问题, 更一般地说, 它也可以提供与最佳离线分配相对的严格比率。 而早期的问题只将最差案例的构建作为优化问题。 通过这一分析, 我们的文件提供了一个统一的框架, 可以产生新的结果, 并收回许多现有的。 首先, 我们表明, 在Cawla 等人( 202020 ) 的“ 明显” 设置固定门槛时, 最明显的“ ” 方法, 在所有固定门槛值框架中, 最能提供最精确的“ 精确的“ ” 的“ I 的“ 的” 的“ 的” 的“ 标准值” 的“ 值” 在任何固定阈值” 计算结果中, 我们的“ 我们的“ 确定任何固定的“ 的“ 的“ 的” 的” 的“ 的” 的“ 的” 的“ 的” 的“ 的“ 的“ 的” 确定” 的” 的“ 的“ 的” 的“ 的” 的” 的” 的“ 的” 和” 的“ 的“ 的“ 的“ 的“ 的” 的” 的” 的” 的” 的“ 的“ 的” 的” 的“ 的” 的” 的” 的” 的“ 的” 的” 的“ 的“ 的” 的” 的” 的“ 的” 的” 的“ 的“ 的” 的” 的” 的“ 的“ 的“ 的” 的” 的“ 的” 的“ 的”