整数网格还原的高效算法

IF 1.5 2区 数学 Q2 MATHEMATICS, APPLIED SIAM Journal on Matrix Analysis and Applications Pub Date : 2024-01-24 DOI:10.1137/23m1557933
François Charton, Kristin Lauter, Cathy Li, Mark Tygert
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引用次数: 0

摘要

SIAM 矩阵分析与应用期刊》,第 45 卷,第 1 期,第 353-367 页,2024 年 3 月。 摘要整数网格是一组向量的所有线性组合的集合,其中向量的所有条目都是整数,线性组合中的所有系数也都是整数。网格还原指的是在给定网格中找到一个向量集,使这个子集的所有整数线性组合集合仍然是整个原始网格,并使子集的欧几里得规范减小。本文提出了简单、高效的网格还原迭代法,保证每次迭代都能单调地降低基向量(子集中的向量)的欧氏规范。每次迭代都会选择一个基向量,在这个基向量上,沿所选向量投影掉(整数系数)其他基向量的分量,可以使还原后基向量的欧氏常态最小化。每次迭代都会沿着选定的基向量投影出分量,并有效地更新下一次迭代所需的所有信息,以选择最佳基向量并执行相关的投影。
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An Efficient Algorithm for Integer Lattice Reduction
SIAM Journal on Matrix Analysis and Applications, Volume 45, Issue 1, Page 353-367, March 2024.
Abstract. A lattice of integers is the collection of all linear combinations of a set of vectors for which all entries of the vectors are integers and all coefficients in the linear combinations are also integers. Lattice reduction refers to the problem of finding a set of vectors in a given lattice such that the collection of all integer linear combinations of this subset is still the entire original lattice and so that the Euclidean norms of the subset are reduced. The present paper proposes simple, efficient iterations for lattice reduction which are guaranteed to reduce the Euclidean norms of the basis vectors (the vectors in the subset) monotonically during every iteration. Each iteration selects the basis vector for which projecting off (with integer coefficients) the components of the other basis vectors along the selected vector minimizes the Euclidean norms of the reduced basis vectors. Each iteration projects off the components along the selected basis vector and efficiently updates all information required for the next iteration to select its best basis vector and perform the associated projections.
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来源期刊
CiteScore
2.90
自引率
6.70%
发文量
61
审稿时长
6-12 weeks
期刊介绍: The SIAM Journal on Matrix Analysis and Applications contains research articles in matrix analysis and its applications and papers of interest to the numerical linear algebra community. Applications include such areas as signal processing, systems and control theory, statistics, Markov chains, and mathematical biology. Also contains papers that are of a theoretical nature but have a possible impact on applications.
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