论文标题
全景中的AdWords
Adwords in a Panorama
论文作者
论文摘要
三十年前，Karp，Vazirani和Vazirani（Stoc 1990）定义了在线匹配问题，并给出了最佳的$ 1- \ frac {1} {1} {e} {e} \大约0.632 $ -Competive AlgorithM。十五年后，Mehta，Saberi，Vazirani和Vazirani（Focs 2005）引入了第一个由在线广告驱动的AdWords的概括，并获得了最佳的$ 1- \ frac {1} {1} {e} $竞争性比率，在特殊情况下是小型竞争的特殊情况。从那以后，它一直开放，是否有一般投标算法要比$ 0.5 $竞争的贪婪算法更好。本文介绍了$ 0.5016 $竞争算法的AdWords，并在积极的范围内回答了这个空旷的问题。该算法建立在几种成分的基础上，包括在线原始双重框架和Huang和Zhang（STOC 2020）最近探索的匹配问题的配置线性程序，这是一种新颖的AdWords，我们称之为Panorama View，以及Fahrbach，Zady call，ta的fahrbach，weean和Zad ne wee，weean和fahrbach，weean和fahrbach，weean和f。全景在线相关选择。
Three decades ago, Karp, Vazirani, and Vazirani (STOC 1990) defined the online matching problem and gave an optimal $1-\frac{1}{e} \approx 0.632$-competitive algorithm. Fifteen years later, Mehta, Saberi, Vazirani, and Vazirani (FOCS 2005) introduced the first generalization called AdWords driven by online advertising and obtained the optimal $1-\frac{1}{e}$ competitive ratio in the special case of small bids. It has been open ever since whether there is an algorithm for general bids better than the $0.5$-competitive greedy algorithm. This paper presents a $0.5016$-competitive algorithm for AdWords, answering this open question on the positive end. The algorithm builds on several ingredients, including a combination of the online primal dual framework and the configuration linear program of matching problems recently explored by Huang and Zhang (STOC 2020), a novel formulation of AdWords which we call the panorama view, and a generalization of the online correlated selection by Fahrbach, Huang, Tao, and Zadimorghaddam (FOCS 2020) which we call the panoramic online correlated selection.
