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  • 标题:Search-to-Decision Reductions for Lattice Problems with Approximation Factors (Slightly) Greater Than One
  • 本地全文:下载
  • 作者:Noah Stephens-Davidowitz
  • 期刊名称:LIPIcs : Leibniz International Proceedings in Informatics
  • 电子版ISSN:1868-8969
  • 出版年度:2016
  • 卷号:60
  • 页码:19:1-19:18
  • DOI:10.4230/LIPIcs.APPROX-RANDOM.2016.19
  • 出版社:Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
  • 摘要:We show the first dimension-preserving search-to-decision reductions for approximate SVP and CVP. In particular, for any gamma <= 1 + O(log n/n), we obtain an efficient dimension-preserving reduction from gamma^{O(n/log n)}-SVP to gamma-GapSVP and an efficient dimension-preserving reduction from gamma^{O(n)}-CVP to gamma-GapCVP. These results generalize the known equivalences of the search and decision versions of these problems in the exact case when gamma = 1. For SVP, we actually obtain something slightly stronger than a search-to-decision reduction - we reduce gamma^{O(n/log n)}-SVP to gamma-unique SVP, a potentially easier problem than gamma-GapSVP.
  • 关键词:Lattices; SVP; CVP
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