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A lower bound on the competitivity of memory less algorithms for a generalization of the CNN problem

机译:CNN问题泛化的无内存算法竞争能力的下限

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摘要

We consider the CNN problem in arbitrary dimension, and over any metric space containing the integers. We prove that, in every dimension at least 2, no memoryless online algorithm can achieve a constant competitive ratio, under a weak symmetry constraint on the algorithm. This generalizes in several aspects the lower bounds obtained by Koutsoupias and Taylor [The CNN Problem and other k-server variants, Theoret. Comput. Sci. 324 (2004) 347-359] for the original problem. The proof consists in the analysis of carefully selected random walks, which appear naturally in the framework of memoryless algorithms.
机译:我们在任意维度上以及在包含整数的任何度量空间上考虑CNN问题。我们证明,在算法的弱对称约束下,至少在每个维度上,没有任何一种无记忆在线算法可以实现恒定的竞争比。这从几个方面概括了Koutsoupias和Taylor [CNN问题和其他k服务器变体Theoret。计算科学324(2004)347-359]。证明在于对精心挑选的随机游动进行分析,这些随机游动自然出现在无记忆算法的框架中。

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