关于一般正权格规则的CBC-DBD构造的注记

IF 1.8 2区 数学 Q1 MATHEMATICS
Peter Kritzer
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引用次数: 0

摘要

格规则是研究最突出的拟蒙特卡罗方法来近似多元积分。s维积分的秩-1格规则由其生成向量z∈z及其点数n来指定。虽然有许多关于“好的”秩-1格规则存在的结果,但对于维数s≥3的秩-1格规则没有明确的构造。因此,人们求助于计算机搜索算法。在Ebert等人最近发表在《复杂性杂志》上的一篇论文中,我们展示了一种组件-组件-数字-数字(CBC-DBD)构造,它可以很好地生成向量,用于在带有乘积权重的加权Korobov类中对函数进行积分。在这里,我们将这个结果推广到任意的正权重,回答了Ebert等人的论文中的一个开放问题。我们包含了关于如何在POD权重的情况下实现算法的一节,这意味着CBC- dbd结构与经典CBC结构是竞争的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A note on the CBC-DBD construction of lattice rules with general positive weights

Lattice rules are among the most prominently studied quasi-Monte Carlo methods to approximate multivariate integrals. A rank-1 lattice rule for an s-dimensional integral is specified by its generating vector zZs and its number of points N. While there are many results on the existence of “good” rank-1 lattice rules, there are no explicit constructions of good generating vectors for dimensions s3. Therefore one resorts to computer search algorithms. In a recent paper by Ebert et al. in the Journal of Complexity, we showed a component-by-component digit-by-digit (CBC-DBD) construction for good generating vectors for integration of functions in weighted Korobov classes equipped with product weights. Here, we generalize this result to arbitrary positive weights, answering an open question from the paper of Ebert et al. We include a section on how the algorithm can be implemented in the case of POD weights, implying that the CBC-DBD construction is competitive with the classical CBC construction.

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来源期刊
Journal of Complexity
Journal of Complexity 工程技术-计算机:理论方法
CiteScore
3.10
自引率
17.60%
发文量
57
审稿时长
>12 weeks
期刊介绍: The multidisciplinary Journal of Complexity publishes original research papers that contain substantial mathematical results on complexity as broadly conceived. Outstanding review papers will also be published. In the area of computational complexity, the focus is on complexity over the reals, with the emphasis on lower bounds and optimal algorithms. The Journal of Complexity also publishes articles that provide major new algorithms or make important progress on upper bounds. Other models of computation, such as the Turing machine model, are also of interest. Computational complexity results in a wide variety of areas are solicited. Areas Include: • Approximation theory • Biomedical computing • Compressed computing and sensing • Computational finance • Computational number theory • Computational stochastics • Control theory • Cryptography • Design of experiments • Differential equations • Discrete problems • Distributed and parallel computation • High and infinite-dimensional problems • Information-based complexity • Inverse and ill-posed problems • Machine learning • Markov chain Monte Carlo • Monte Carlo and quasi-Monte Carlo • Multivariate integration and approximation • Noisy data • Nonlinear and algebraic equations • Numerical analysis • Operator equations • Optimization • Quantum computing • Scientific computation • Tractability of multivariate problems • Vision and image understanding.
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