用Caratheodory定理的近似逼近纳什均衡和密集二部子图

Siddharth Barman
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引用次数: 71

摘要

我们提出了卡拉西奥多定理的一个近似版本的算法应用。定理指出,给定一组向量X在Rd,每个向量X有一个凸包的ε关闭(在p-norm距离,2≤p <∞)向量可以表示为最多的凸组合向量b (X),结合b取决于ε和常态p和独立的维d。这个定理可以被实例化派生Maurey引理,早期工作的引用,可以发现琳(1981)和卡尔(1985)。然而,在本文中,我们给出了这个结果的一个自包含证明。利用这一定理,我们证明了在具有n × n个收益矩阵a, B的双矩阵对策中,如果a +B的任意列中的非零条目数最多为s,则可以在nO(log s/ε2})时间内计算出对策的ε-纳什均衡。特别是,这为具有固定列稀疏性s的博弈中的纳什均衡提供了一个多项式时间近似方案。此外,对于任意双矩阵博弈(因为s最多可以是n),我们的算法的运行时间与Lipton, Markakis和Mehta(2003)获得的最著名的上界相匹配。近似carathacimodory定理也导致了最密集k-二部子图问题的加性逼近算法。给定一个有n个顶点,最大度为d的图,该算法确定一个k x k的二部子图,其密度在时间nO(log d/ε2)的最优密度ε(在加性意义上)内。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Approximating Nash Equilibria and Dense Bipartite Subgraphs via an Approximate Version of Caratheodory's Theorem
We present algorithmic applications of an approximate version of Caratheodory's theorem. The theorem states that given a set of vectors X in Rd, for every vector in the convex hull of X there exists an ε-close (under the p-norm distance, for 2 ≤ p < ∞) vector that can be expressed as a convex combination of at most b vectors of X, where the bound b depends on ε and the norm p and is independent of the dimension d. This theorem can be derived by instantiating Maurey's lemma, early references to which can be found in the work of Pisier (1981) and Carl (1985). However, in this paper we present a self-contained proof of this result. Using this theorem we establish that in a bimatrix game with n x n payoff matrices A, B, if the number of non-zero entries in any column of A+B is at most s then an ε-Nash equilibrium of the game can be computed in time nO(log s/ε2}). This, in particular, gives us a polynomial-time approximation scheme for Nash equilibrium in games with fixed column sparsity s. Moreover, for arbitrary bimatrix games---since s can be at most n---the running time of our algorithm matches the best-known upper bound, which was obtained by Lipton, Markakis, and Mehta (2003). The approximate Carathéodory's theorem also leads to an additive approximation algorithm for the densest k-bipartite subgraph problem. Given a graph with n vertices and maximum degree d, the developed algorithm determines a k x k bipartite subgraph with density within ε (in the additive sense) of the optimal density in time nO(log d/ε2).
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