Efficient Multiscale Lanczos Eigenpair Extraction

IF 13 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Theo Braune, Jérémie Dumas, Jean-Marc Thiery
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引用次数: 0

Abstract

Eigenpair extractions are crucial for various applications in geometry processing and graphics. State of the Art libraries like ARPACK or Spectra rely on the implicitly restarted Lanczos iteration to extract eigenpairs efficiently. However for some large scale problems they lack convergence speed and robustness. In this paper we present a simple multigrid extension to accelerate the convergence and robustness of the implicitly restarted Lanczos method, and we demonstrate the efficiency of our method on a variety of problems commonly found in geometry processing and graphics.
高效多尺度Lanczos特征对提取
特征对提取在几何处理和图形学的各种应用中是至关重要的。像ARPACK或Spectra这样的最先进的库依赖于隐式重新启动的Lanczos迭代来有效地提取特征对。但对于一些大规模问题,它们缺乏收敛速度和鲁棒性。本文提出了一种简单的多网格扩展,以加速隐式重新启动Lanczos方法的收敛性和鲁棒性,并证明了该方法在几何处理和图形学中常见的各种问题上的有效性。
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
ACM Transactions on Graphics
ACM Transactions on Graphics 工程技术-计算机:软件工程
CiteScore
14.30
自引率
25.80%
发文量
193
审稿时长
12 months
期刊介绍: ACM Transactions on Graphics (TOG) is a peer-reviewed scientific journal that aims to disseminate the latest findings of note in the field of computer graphics. It has been published since 1982 by the Association for Computing Machinery. Starting in 2003, all papers accepted for presentation at the annual SIGGRAPH conference are printed in a special summer issue of the journal.
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