A Primal Dual Approach for Dynamic Bid Optimization

Lingfei Yu, Kun She, Changyuan Yu
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Abstract

We study the dynamic bid optimization problem via a primal dual approach. In the case we have no information about the distribution of queries, we reconstruct the ln(U=L) + 1 competitive algorithm proposed in [ZCL08] through a systematic way and showed the intuition behind this algorithm. In the case of random permutation model, we showed that the learning technique used in [DH09] can give us a (1 ¡ O(²)) competitive algorithm for any small constant ² > 0 as long as the optimum is large enough.
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动态竞价优化的原始对偶方法
利用原始对偶方法研究了动态竞价优化问题。在没有查询分布信息的情况下,我们通过系统的方式重构了[ZCL08]中提出的ln(U=L) + 1竞争算法,并展示了该算法背后的直觉。在随机排列模型的情况下,我们证明了[DH09]中使用的学习技术可以为我们提供一个(1±(²))竞争算法,对于任何小常数²> 0,只要最优值足够大。
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