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Math @ Duke
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Publications [#330134] of Alessandro Arlotto
Papers Published
- Arlotto, A; Wei, Y; Xie, X, An adaptive O(log n)-optimal policy for the online selection of a monotone subsequence from a random sample,
Random Structures and Algorithms, vol. 52 no. 1
(January, 2018),
pp. 41-53, WILEY [doi]
(last updated on 2026/02/08)
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 (Figure presented.) of optimal. Our construction provides a direct and natural way for proving the (Figure presented.) -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.
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