Optimal Online Selection of an Alternating Subsequence: A Central Limit Theorem

Abstract

We analyze the optimal policy for the sequential selection of an alternating subsequence from a sequence ofnindependent observations from a continuous distributionF, and we prove a central limit theorem for the number of selections made by that policy. The proof exploits the backward recursion of dynamic programming and assembles a detailed understanding of the associated value functions and selection rules.