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Numerical Algorithms· 2026Q1

Computation of the zeros of Laguerre–Sobolev polynomials by the Ehrlich–Aberth method

T. Laudadio, Nicola Mastronardi, F. Marcellán, N. Van Buggenhout et al.

Short summary

A new algorithm using the Ehrlich–Aberth method computes all zeros of Laguerre–Sobolev orthogonal polynomials efficiently and accurately, avoiding overflow issues with high-degree polynomials.

AI-generated from the title and abstract; the full text is not read.

Key points

  • A new algorithm based on the Ehrlich–Aberth method is proposed for computing zeros of Laguerre–Sobolev orthogonal polynomials.
  • Novel recurrence relations are introduced to compute the ratio of polynomials and their derivatives, preventing overflow for degrees > 170.
  • The algorithm exhibits O(n^2) computational complexity and O(n) memory requirements.
  • The method is demonstrated to be efficient and accurate.

AI-generated from the title and abstract; the full text is not read.

Abstract

Abstract A new algorithm for computing all the zeros of Laguerre–Sobolev orthogonal polynomials, based on the Ehrlich–Aberth method, is described in this work. The Ehrlich–Aberth method is a Newton–like method, requiring, at each iteration, the evaluation of the polynomial and its derivative in the computed approximations of the zeros. The Laguerre–Sobolev polynomials are related to the classical Laguerre orthogonal polynomials by a connection formula, that allows to evaluate the former polynomials and their derivatives in a point. This relation can be then exploited in the Ehrlich–Aberth method. Laguerre–Sobolev polynomials exhibit a behavior similar to that of Laguerre polynomials: their values grow rapidly as their degrees increase, and overflow occurs in floating point arithmetic if their degree exceeds 170. In order to avoid overflow, novel recurrence relations are proposed to simultaneously compute the ratio between the Laguerre–Sobolev polynomials and the corresponding derivatives in a point. The proposed algorithm turns out to be very efficient and accurate, with $$ \varvec{\mathcal {O}}\varvec{(}\varvec{n}^{\varvec{2}}\varvec{)} $$ O ( n 2 ) computational complexity and $$ \varvec{\mathcal {O}}\varvec{(n)} $$ O ( n ) memory, where $$\varvec{n}$$ n is the degree of the polynomial.

The authors' abstract, as published at the source. Numerical Algorithms, 2026 · DOI ↗

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Field: Applied Mathematics

Applied MathematicsMathematics