On the Fast Evaluation of Polynomials

P. Abascal Fuentes *

Department of Mathematics, University of Oviedo, Edificio Polivalente Campus de Viesques, 33203-Gijon, Asturias, Espana.

F. Fueyo Tirado

Department of Mathematics, University of Oviedo, Edificio Polivalente Campus de Viesques, 33203-Gijon, Asturias, Espana.

D. Garcia Quintas

Department of Mathematics, University of Oviedo, Edificio Polivalente Campus de Viesques, 33203-Gijon, Asturias, Espana.

J. Jimenez Meana

Department of Mathematics, University of Oviedo, Edificio Polivalente Campus de Viesques, 33203-Gijon, Asturias, Espana.

A. Palacio Muniz

Department of Mathematics, University of Oviedo, Edificio Polivalente Campus de Viesques, 33203-Gijon, Asturias, Espana.

*Author to whom correspondence should be addressed.


Abstract

Minimizing the computational cost of polynomial evaluation is a main problem in Computational Science.

Horner's algorithm efficiently solves the problem of evaluating a polynomial of degree n in time O(n). It is also used to evaluate multivariate polynomials and, as an extension, matrix polynomials.

If, in addition, a parallel execution of this method can be carried out, the computational cost of this problem would be further minimized.

This makes it especially important to develop a strategy for parallel execution of Horner's method for fast and ecient polynomial evaluation.

In this paper we present a parallelization of Horner's method based on polynomial partitioning, as well as a modification of this method to exploit its advantages in the evaluation of sparse polynomials.

We also provide an analysis of the numerical error between the proposed parallel method and the classic algorithm.

Keywords: Parallel polynomial evaluation, parallel algorithms, sparse polynomials


How to Cite

Fuentes, P. Abascal, F. Fueyo Tirado, D. Garcia Quintas, J. Jimenez Meana, and A. Palacio Muniz. 2022. “On the Fast Evaluation of Polynomials”. Journal of Advances in Mathematics and Computer Science 37 (6):20-35. https://doi.org/10.9734/jamcs/2022/v37i630457.

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