Empirical Hardness Models for Combinatorial Auctions
Empirical Hardness Models for Combinatorial Auctions
复制标题
组合拍卖的经验硬度模型
DOI：
10.7551/mitpress/9780262033428.003.0020
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
Y. Shoham
中科院分区：
文献类型：
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作者：
Kevin Leyton;Nudelman Eugene;Y. Shoham
Shoham在本章中，我们考虑赢家决定问题的经验困难。我们识别数据实例的分布非特定特征，然后使用统计回归技术来学习，评估和解释从这些特征到实例的预测硬度的函数，主要集中在CNORG的CPLEX求解器上。我们还描述了这些模型的两个应用：建立一个算法组合，选择不同的WDP算法，并诱导测试分布，更难为这个算法组合。
Shoham In this chapter we consider the empirical hardness of the winner determination problem. We identify distribution-nonspecific features of data instances and then use statistical regression techniques to learn, evaluate and interpret a function from these features to the predicted hardness of an instance, focusing mostly on ILOG's CPLEX solver. We also describe two applications of these models: building an algorithm portfolio that selects among different WDP algorithms, and inducing test distributions that are harder for this algorithm portfolio.