A game-theoretic classification of interactive complexity classes
SCT '95 Proceedings of the 10th Annual Structure in Complexity Theory Conference (SCT'95)
Adaptive routing with end-to-end feedback: distributed learning and geometric approaches
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
On the Complexity of Succinct Zero-Sum Games
CCC '05 Proceedings of the 20th Annual IEEE Conference on Computational Complexity
Efficient algorithms for online decision problems
Journal of Computer and System Sciences - Special issue: Learning theory 2003
Artificial Intelligence
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
STACS'99 Proceedings of the 16th annual conference on Theoretical aspects of computer science
Secretary problems with competing employers
WINE'06 Proceedings of the Second international conference on Internet and Network Economics
Bilinear games: polynomial time algorithms for rank based subclasses
WINE'11 Proceedings of the 7th international conference on Internet and Network Economics
Risk sensitivity of price of anarchy under uncertainty
Proceedings of the fourteenth ACM conference on Electronic commerce
User satisfaction in competitive sponsored search
Proceedings of the 23rd international conference on World wide web
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We revisit classic algorithmic search and optimization problems from the perspective of competition. Rather than a single optimizer minimizing expected cost, we consider a zero-sum game in which a search problem is presented to two players, whose only goal is to outperform the opponent. Such games are typically exponentially large zero-sum games, but they often have a rich structure. We provide general techniques by which such structure can be leveraged to find minmax-optimal and approximate minmax-optimal strategies. We give examples of ranking, hiring, compression, and binary search duels, among others. We give bounds on how often one can beat the classic optimization algorithms in such duels.