Lattice enumeration via linear programming

IF 2.1 2区 数学 Q1 MATHEMATICS, APPLIED
Moulay Abdellah Chkifa
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引用次数: 0

Abstract

Given a positive integer d and \({{\varvec{a}}}_{1},\dots ,{{\varvec{a}}}_{r}\) a family of vectors in \({{\mathbb {R}}}^d\), \(\{k_1{{\varvec{a}}}_{1}+\dots +k_r{{\varvec{a}}}_{r}: k_1,\dots ,k_r \in {{\mathbb {Z}}}\}\subset {{\mathbb {R}}}^d\) is the so-called lattice generated by the family. In high dimensional integration, prescribed lattices are used for constructing reliable quadrature schemes. The quadrature points are the lattice points lying on the integration domain, typically the unit hypercube \([0,1)^d\) or a rescaled shifted hypercube. It is crucial to have a cost-effective method for enumerating lattice points within such domains. Undeniably, the lack of such fast enumeration procedures hinders the applicability of lattice rules. Existing enumeration procedures exploit intrinsic properties of the lattice at hand, such as periodicity, orthogonality, recurrences, etc. In this paper, we unveil a general-purpose fast lattice enumeration algorithm based on linear programming (named FLE-LP).

Abstract Image

通过线性规划进行网格枚举
给定一个正整数 d 和 \({{\varvec{a}}}_{1},\dots ,{{{\varvec{a}}}_{r}\) 中的一个向量族, \(\{k_1{{varvec{a}}}_{1}+\dots +k_r{{\varvec{a}}}_{r}:k_1,\dots ,k_r \in {{\mathbb {Z}}} 子集 {{\mathbb {R}}}^d\) 是由族产生的所谓晶格。在高维积分中,规定网格用于构建可靠的正交方案。正交点是位于积分域上的网格点,通常是单位超立方体(\([0,1)^d\)或重比例移位超立方体。拥有一种经济有效的方法来枚举这些域内的网格点至关重要。不可否认,缺乏这种快速枚举程序阻碍了网格规则的应用。现有的枚举程序利用的是网格的内在属性,如周期性、正交性、递归性等。本文揭示了一种基于线性规划的通用快速网格枚举算法(命名为 FLE-LP)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Numerische Mathematik
Numerische Mathematik 数学-应用数学
CiteScore
4.10
自引率
4.80%
发文量
72
审稿时长
6-12 weeks
期刊介绍: Numerische Mathematik publishes papers of the very highest quality presenting significantly new and important developments in all areas of Numerical Analysis. "Numerical Analysis" is here understood in its most general sense, as that part of Mathematics that covers: 1. The conception and mathematical analysis of efficient numerical schemes actually used on computers (the "core" of Numerical Analysis) 2. Optimization and Control Theory 3. Mathematical Modeling 4. The mathematical aspects of Scientific Computing
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