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An adaptive O(log n)‐optimal policy for the online selection of a monotone subsequence from a random sample
Abstract:Given a sequence of n independent random variables with common continuous distribution, we propose a simple adaptive online policy that selects a monotone increasing subsequence. We show that the expected number of monotone increasing selections made by such a policy is within urn:x-wiley:10429832:media:rsa20728:rsa20728-math-0002 of optimal. Our construction provides a direct and natural way for proving the urn:x-wiley:10429832:media:rsa20728:rsa20728-math-0003‐optimality gap. An earlier proof of the same result made crucial use of a key inequality of Bruss and Delbaen 5] and of de‐Poissonization.
Keywords:adaptive policy  dynamic programming  Markov decision problem  monotone subsequence  online selection
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