Rectangular eigenvalue problems
Behnam Hashemi, Yuji Nakatsukasa, Lloyd N. Trefethen · Advances in Computational Mathematics · 2022
Abstract Often the easiest way to discretize an ordinary or partial differential equation is by a rectangular numerical method, in which n basis functions are sampled at m ≫ n collocation points. We show how eigenvalue problems can be solved in this setting by QR reduction to square matrix generalized eigenvalue problems. The method applies equally in the limit “ $m=\infty $ m = ∞ ” of eigenvalue problems for quasimatrices. Numerical examples are presented as well as pointers to related literature.