On the local convergence of a quasi-Newton method for solving matrix polynomial equations

E.M. Macías, R. Pérez and H.J. Martínez

Applied Mathematics and Computation, 2023, vol. 441, issue C

Abstract: In this article, we propose a quasi-Newton algorithm to solve a matrix polynomial equation, which can be seen as a generalization of the algorithm of the same type to solve the matrix quadratic equation proposed in Macías et al. (2016). The proposed algorithm reduces the computational cost of the Newton–Schur method traditionally used to solve this type of equations. We show that this algorithm is local and even quadratically convergent. Finally, we present numerical experiments that ratify the theoretical results developed.

Keywords: Matrix polynomial equations; Quasi-Newton algorithm; Local convergence (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300322007469
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:441:y:2023:i:c:s0096300322007469

DOI: 10.1016/j.amc.2022.127678

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().