A dynamic learning algorithm for online matching problems with concave returns

•We consider the online matching problem with concave returns. It is the core model for ad allocation.•We propose a dynamic learning algorithm that achieves near-optimal performance for this problem.•Our approach is primal-dual based. We overcome the difficulty that arises due to the nonlinearity.•W...

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Bibliographic Details
Published in:	European journal of operational research Vol. 247; no. 2; pp. 379 - 388
Main Authors:	Chen, Xiao Alison, Wang, Zizhuo
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 01.12.2015 Elsevier Sequoia S.A
Subjects:	Adwords problem Algorithms Allocations Artificial intelligence Decision analysis Dynamic price update Mathematical functions Mathematical problems Online algorithms Optimization algorithms Primal-dual Random permutation model Studies Online algorithms Adwords problem Dynamic price update Random permutation model Primal-dual
ISSN:	0377-2217, 1872-6860
Online Access:	Get full text
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