随机顺序模型
Random-Order Models
摘要：
本章介绍了在线算法中的\emph{random-order model}。在这个模型中，输入由对手选择，然后在提交给算法之前随机排列。这种重新洗牌通常会削弱对手的力量，并允许改进算法保证。我们展示了两大类问题的改进：包装问题，我们必须选择一组受约束的项目，以最大限度地提高总价值，并覆盖问题，我们必须满足给定的要求，以最小的总成本。我们还讨论了随机序模型与其他用于非最坏情况竞争分析的随机模型的关系。
英文摘要：
This chapter introduces the \emph{random-order model} in online algorithms. In this model, the input is chosen by an adversary, then randomly permuted before being presented to the algorithm. This reshuffling often weakens the power of the adversary and allows for improved algorithmic guarantees. We show such improvements for two broad classes of problems: packing problems where we must pick a constrained set of items to maximize total value, and covering problems where we must satisfy given requirements at minimum total cost. We also discuss how random-order model relates to other stochastic models used for non-worst-case competitive analysis.
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。
