Simple strategies for large zero-sum games with applications to complexity theory

Von Neumann's Min-Max Theorem guarantees that each player of a zero-sum matrix game has an optimal mixed strategy. This paper gives an elementary proof that each player has a near-optimal mixed strategy that chooses uniformly at random from a multiset of pure strategies of size logarithmic in the number of pure strategies available to the opponent. For exponentially large games, for which even representing an optimal mixed strategy can require exponential space, it follows that there are near-optimal, linear-size strategies. These strategies are easy to play and serve as small witnesses to the approximate value of the game. As a corollary, it follows that every language has small ``hard'' multisets of inputs certifying that small circuits can't decide the language. For example, if SAT does not have polynomial-size circuits, then, for each n and c, there is a set of n^(O(c)) Boolean formulae of size n such that no circuit of size n^c (or algorithm running in time n^c) classifies more than two-thirds of the formulae succesfully.

[1]  W. Hoeffding Probability Inequalities for sums of Bounded Random Variables , 1963 .

[2]  N. S. Barnett,et al.  Private communication , 1969 .

[3]  Leonard M. Adleman,et al.  Two theorems on random polynomial time , 1978, 19th Annual Symposium on Foundations of Computer Science (sfcs 1978).

[4]  Andrew C. Yao,et al.  Lower bounds by probabilistic arguments , 1983, 24th Annual Symposium on Foundations of Computer Science (sfcs 1983).

[5]  Silvio Micali,et al.  The knowledge complexity of interactive proof-systems , 1985, STOC '85.

[6]  Yuri Gurevich Complete and incomplete randomized NP problems , 1987, 28th Annual Symposium on Foundations of Computer Science (sfcs 1987).

[7]  Leonid A. Levin,et al.  Random instances of a graph coloring problem are hard , 1988, STOC '88.

[8]  Uwe Schöning Probabilistic Complexity Classes and Lowness , 1989, J. Comput. Syst. Sci..

[9]  Ming Li,et al.  A theory of learning simple concepts under simple distributions and average case complexity for the universal distribution , 1989, 30th Annual Symposium on Foundations of Computer Science.

[10]  Oded Goldreich,et al.  On the theory of average case complexity , 1989, STOC '89.

[11]  Manuel Blum,et al.  Designing programs that check their work , 1989, STOC '89.

[12]  U. Schoning Probalisitic complexity classes and lowness , 1989 .

[13]  Yuri Gurevich Matrix decomposition problem is complete for the average case , 1990, Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science.

[14]  Leonid A. Levin,et al.  No better ways to generate hard NP instances than picking uniformly at random , 1990, Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science.

[15]  Robert E. Schapire,et al.  The strength of weak learnability , 1990, Mach. Learn..

[16]  I. Althöfer On sparse approximations to randomized strategies and convex combinations , 1994 .

[17]  N. Young Greedy Algorithms by Derandomizing Unknown Distributions , 1994 .

[18]  Noga Alon,et al.  A Graph-Theoretic Game and Its Application to the k-Server Problem , 1995, SIAM J. Comput..