This paper is concerned with the asymptotic optimality of spectral coarse spaces for two-level iterative methods. Spectral coarse spaces, namely coarse spaces obtained as the span of the slowest modes of the used one-level smoother, are known to be very efficient and, in some cases, optimal. However, the results of this paper show that spectral coarse spaces do not necessarily minimize the asymptotic contraction factor of a two-level iterative method. Moreover, numerical experiments show that there exist coarse spaces that are asymptotically more efficient and lead to preconditioned systems with improved conditioning properties.
翻译:本文关注光谱粗糙空间对于两级迭代方法的无症状最佳性。光谱粗粗空间,即作为使用过的单层平滑度最慢模式的宽度而获得的粗粗空间,据知非常高效,在某些情况下是最佳的。然而,本文的结果表明,光谱粗粗粗空间不一定能将两级迭代方法的无症状收缩因子降到最低。此外,数字实验还表明,存在无症状的粗粗空空间,这些空间在性质上是尽可能高效的,并导致具有改进性能的前提条件系统。