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arXiv 1408.2690cs.GT
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真实社会福利逼近的快速凸分解

Fast Convex Decomposition for Truthful Social Welfare Approximation

Dennis Kraft, Salman Fadaei, Martin Bichler

摘要:

在算法机制设计中,在保持真实性的同时近似最优社会福利是一个研究得很好的问题。Lavi和Swamy~ \cite{Lavi11}假设给定的机制设计问题的社会福利可由整数规划优化,其整数差距最大为$α$,提出了一种设计随机化$α$ -近似机构的一般方法。他们的方法是基于将松弛线性规划的最优解分解为整数解的凸组合。不幸的是,Lavi和Swamy的分解技术严重依赖于椭球方法,该方法因其较差的实际性能而臭名昭着。为了克服这个问题,我们提出了一种替代的分解技术,它产生一个$α(1 + ε)$近似,只需要一个完整性间隙验证调用的二次数。

英文摘要:

Approximating the optimal social welfare while preserving truthfulness is a well studied problem in algorithmic mechanism design. Assuming that the social welfare of a given mechanism design problem can be optimized by an integer program whose integrality gap is at most $α$, Lavi and Swamy~\cite{Lavi11} propose a general approach to designing a randomized $α$-approximation mechanism which is truthful in expectation. Their method is based on decomposing an optimal solution for the relaxed linear program into a convex combination of integer solutions. Unfortunately, Lavi and Swamy's decomposition technique relies heavily on the ellipsoid method, which is notorious for its poor practical performance. To overcome this problem, we present an alternative decomposition technique which yields an $α(1 + ε)$ approximation and only requires a quadratic number of calls to an integrality gap verifier.

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