首页> 外文会议>International Colloquium on Automata, Languages and Programming(ICALP 2007); 20070709-13; Wroclaw(PL) >Sampling Methods for Shortest Vectors, Closest Vectors and Successive Minima
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Sampling Methods for Shortest Vectors, Closest Vectors and Successive Minima

机译:最短向量,最近向量和连续最小值的采样方法

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In this paper we introduce a new lattice problem, the sub-space avoiding problem (Sap). We describe a probabilistic single exponential time algorithm for Sap for arbitrary d_p norms. We also describe polynomial time reductions for four classical problems from the geometry of numbers, the shortest vector problem (Svp), the closest vector problem (Cvp), the successive minima problem (Smp), and the shortest independent vectors problem (Sivp) to Sap, establishing probabilistic single exponential time algorithms for them. The result generalize and extend previous results of Ajtai, Kumar and Sivakumar. The results on Smp and Sivp are new for all norms. The results on Svp and Cvp generalize previous results of Ajtai et al. for the e_2 norm to arbitrary e_p norms.
机译:在本文中,我们介绍了一个新的格点问题,即子空间回避问题(Sap)。我们描述了针对任意d_p范数的Sap的概率单指数时间算法。我们还描述了从数的几何,最短向量问题(Svp),最接近向量问题(Cvp),连续最小问题(Smp)和最短独立向量问题(Sivp)到四个经典问题的多项式时间约简树汁,为其建立概率单指数时间算法。该结果推广并扩展了Ajtai,Kumar和Sivakumar的先前结果。 Smp和Sivp的结果对于所有规范都是新的。 Svp和Cvp的结果推广了Ajtai等人先前的结果。将e_2规范转换为任意e_p规范。

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