Discrete least-squares technique for eigenvalues. Part I: The one-dimensional case

Abstract

A discrete least-squares technique for computing the eigenvalues of differential equations is presented. The eigenvalue approximations are obtained in two steps. Firstly, initial approximations of the desired eigenvalues are computed by solving a quadratic matrix eigenvalue problem resulting from the least-squares method applied to the equation under consideration. Secondly, these initial approximations, being of sufficient accuracy in some cases, are improved by using the Gauss-Newton method. Results from numerical experiments are reported that show great efficiency of the proposed technique in solving both regular and singular one-dimensional problems. The high flexibility of the technique enables one to use also the multidomain approach and the trial functions not satisfying any of the prescribed boundary conditions.