In this paper we propose and analyze new efficient sparse approximate inverse (SAI) smoothers for solving the two-dimensional (2D) three-dimensional (3D) Laplacian linear system with geometric multigrid methods. Local Fourier analysis shows that our proposed SAI smoother 2D achieves a much smaller smoothing factor than state-of-the-art studied in Bolten et al. (2016) [12]. The 3D cases provides...

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 29 May 2020Accepted: 28 September 2021Published online: 13 December 2021Keywordsspace complexity, derandomization, expander graphs, spectral sparsification, random walks, Laplacian systemsAMS Subject Headings68Q25Publication DataISSN (print): 0097-5397ISSN (online): 1095-...