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Sampling Methods for Shortest Vectors, Closest Vectors and Successive Minima

机译:最短矢量的采样方法,最接近的矢量和连续的最小值

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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晶格问题,子空间避免问题(SAP)。我们描述了一种用于任意D_P规范的SAP的概率单指数时间算法。我们还描述了来自数字几何形状的四个古典问题的多项式时间减少,最短的向量问题(SVP),最接近的矢量问题(CVP),连续的最小问题(SMP)和最短的独立向量问题(SIVP) SAP,建立它们的概率单指数时间算法。结果概括并扩展了Ajtai,Kumar和Sivakumar的先前结果。 SMP和SIVP上的结果对于所有规范都是新的。 SVP和CVP的结果概述了AJTai等人的先前结果。对于任意E_P规范的E_2标准。

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