On the local convergence of a smoothing structured secant method for nonlinear complementarity

R. Pérez, W. Sánchez and H.J. Martínez

Applied Mathematics and Computation, 2025, vol. 488, issue C

Abstract: In this paper, we propose a smoothed structured secant algorithm to solve the nonlinear complementarity problem based on its reformulation as a nonlinear least squares problem. We present the local convergence analysis of the proposed algorithm and preliminary numerical tests that shows a good local performance of the algorithm.

Keywords: Nonlinear complementarity problems; Smoothing methods; Structured secant methods; Nonlinear least squares; Local convergence; Superlinear convergence (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300324005563
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:488:y:2025:i:c:s0096300324005563

DOI: 10.1016/j.amc.2024.129095

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 ().