We consider a significant generalization of the Adwords problem by allowing arbitrary concave returns, and we characterize the optimal competitive ratio achievable. The problem considers a sequence of items arriving online that have to be allocated to agents, with different agents bidding different amounts. The objective function is the sum, over each agent i, of a monotonically nondecreasing concave function Mi : R+ \u2192 R+ of the total amount allocated to i. All variants of online matching problems (including the Adwords problem) studied in the literature consider the special case of budgeted linear functions, that is, functions of the form Mi(ui) = min{ui, Bi} for some constant Bi. The distinguishing feature of this paper is in allowing arbitrary concave returns. The main result of this paper is that for each concave function M, there exists a constant F(M) \u2264 1 such that \u2022 there exists an algorithm with competitive ratio of mini{F(Mi)}, independent of the sequence of items. \u2022 No algorithm has a competitive ratio larger than F(M) over all instances with Mi = M for all i. Our algorithm is based on the primal-dual paradigm and makes use of convex programming duality. The upper bounds are obtained by formulating the task of finding the right counterexample as an optimization problem. This path takes us through the calculus of variations which deals with optimizing over continuous functions. The algorithm and the upper bound are related to each other via a set of differential equations, which points to a certain kind of duality between them<\/p>\n","protected":false},"excerpt":{"rendered":"
We consider a significant generalization of the Adwords problem by allowing arbitrary concave returns, and we characterize the optimal competitive ratio achievable. The problem considers a sequence of items arriving online that have to be allocated to agents, with different agents bidding different amounts. The objective function is the sum, over each agent i, of […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"ACM","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"In Proc. 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