A stochastic algorithm for online bipartite resource allocation problems

This paper deals with online resource allocation problems whereby buyers with a limited total budget want to purchase items which become available one at a time and which consume some amount of various limited resources upon allocation. A central resource allocation platform is in charge of allocating the items to the potential buyers, with the goal of maximizing the total revenue subject to budget and resource constraints. Sponsored search advertising is a typical example: in order to maximize revenue, search engines try to choose the best available advertisement to display on a web page resulting from a search query.Two main approaches have been proposed to address such online problems, depending on the assumptions made about the input sequence: one is trying to guarantee a performance against a worst case scenario (sometimes called the adversarial model); the other one, based on specific probabilistic assumptions about the input, is concerned with expected performance guarantee. However, combining the strengths of these two approaches could potentially outperform both in some settings. In this paper we propose such a practical method which goes beyond the adversarial model but requires only a limited amount of information about the future. We provide extensive computational results which demonstrate settings under which the performance of the proposed algorithm becomes attractive. HighlightsA stochastic online algorithm.A re-optimized primal-dual algorithm.An estimation of the future.Computational experiments.