Randomized online algorithms for the buyback problem

Abstract

In the matroid buyback problem, an algorithm observes a sequence of bids and must decide whether to accept each bid at the moment it arrives, subject to a matroid constraint on the set of accepted bids. Decisions to reject bids are irrevocable, whereas decisions to accept bids may be canceled at a cost which is a fixed fraction of the bid value. We present a new randomized algorithm for this problem, and we prove matching upper and lower bounds to establish that the competitive ratio of this algorithm, against an oblivious adversary, is the best possible. We also observe that when the adversary is adaptive, no randomized algorithm can improve the competitive ratio of the optimal deterministic algorithm. Thus, our work completely resolves the question of what competitive ratios can be achieved by randomized algorithms for the matroid buyback problem. © 2009 Springer-Verlag Berlin Heidelberg.