Multidimensional Binary Search for Contextual Decision-Making

• 成果类型: Article
• 署名作者: Lobel, Ilan; Leme, Renato Paes; Vladua, Adrian
• 署名单位: New York University; Alphabet Inc.; Google Incorporated; Boston University
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2018.1722
• 发表日期: 2018
• 页码: 1346-1361
• 关键词: algorithm

摘要

We consider a multidimensional search problem that is motivated by questions in contextual decision making, such as dynamic pricing and personalized medicine. Nature selects a state from a d-dimensional unit ball and then generates a sequence of d-dimensional directions. We are given access to the directions but not access to the state. After receiving a direction, we have to guess the value of the dot product between the state and the direction. Our goal is to minimize the number of times when our guess is more than e away from the true answer. We construct a polynomial time algorithm that we call projected volume achieving regret O(d log(d/epsilon)), which is optimal up to a log d factor. The algorithm combines a volume cutting strategy with a new geometric technique that we call cylindrification.