Eigenvalues and eigenfunctions of the Laplacian via inverse iteration with shift
This work provides a new computational approach for spectral problems of the Laplacian, potentially benefiting researchers in PDEs and numerical analysis who need efficient eigenvalue solvers.
The paper introduces an iterative method based on inverse iteration with shift to compute eigenvalues and eigenfunctions of the Laplacian on arbitrary bounded domains, avoiding finite-dimensional approximations. The method achieves uniform convergence away from nodal surfaces and offers a simple, fast algorithm with minimal computational requirements.
In this paper we present an iterative method, inspired by the inverse iteration with shift technique of finite linear algebra, designed to find the eigenvalues and eigenfunctions of the Laplacian with homogeneous Dirichlet boundary condition for arbitrary bounded domains $Ω\subset R^{N}$. This method, which has a direct functional analysis approach, does not approximate the eigenvalues of the Laplacian as those of a finite linear operator. It is based on the uniform convergence away from nodal surfaces and can produce a simple and fast algorithm for computing the eigenvalues with minimal computational requirements, instead of using the ubiquitous Rayleigh quotient of finite linear algebra. Also, an alternative expression for the Rayleigh quotient in the associated infinite dimensional Sobolev space which avoids the integration of gradients is introduced and shown to be more efficient. The method can also be used in order to produce the spectral decomposition of any given function $u\in L^{2}(Ω)$.