{"title":"动态竞价优化的原始对偶方法","authors":"Lingfei Yu, Kun She, Changyuan Yu","doi":"10.1109/ICPADS.2010.75","DOIUrl":null,"url":null,"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.","PeriodicalId":365914,"journal":{"name":"2010 IEEE 16th International Conference on Parallel and Distributed Systems","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Primal Dual Approach for Dynamic Bid Optimization\",\"authors\":\"Lingfei Yu, Kun She, Changyuan Yu\",\"doi\":\"10.1109/ICPADS.2010.75\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":365914,\"journal\":{\"name\":\"2010 IEEE 16th International Conference on Parallel and Distributed Systems\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-12-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE 16th International Conference on Parallel and Distributed Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPADS.2010.75\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE 16th International Conference on Parallel and Distributed Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPADS.2010.75","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Primal Dual Approach for Dynamic Bid Optimization
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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