A new self-scaling memoryless quasi-Newton update for unconstrained optimization

M. Jourak (), S. Nezhadhosein () and F. Rahpeymaii ()
Additional contact information
M. Jourak: Payame Noor University
S. Nezhadhosein: Payame Noor University
F. Rahpeymaii: Technical and Vocational University (TVU)

4OR, 2024, vol. 22, issue 2, No 3, 235-252

Abstract: Abstract Based on the augmented version of the quasi-Newton method proposed by Aminifard et al. (App. Num. Math. 167:187–201, 2021), a new scaled parameter of the self-scaling memoryless BFGS update formula is proposed. The idea is to cluster the eigenvalues of the search direction matrix, obtained by minimizing the difference between the largest and the smallest eigenvalues of the matrix. The sufficient descent property is proved for uniformly convex functions, and the global convergence of the proposed algorithm is proved both for the uniformly convex and general nonlinear objective functions. Numerical experiments on a set of test functions of the CUTEr collection show that the proposed method is efficient. In addition, the proposed algorithm is effectively applied to salt and pepper noise elimination problem.

Keywords: Unconstrained optimization; Self-scaling; Quasi-Newton; Noise elimination problem; 90C34; 90C40 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10288-023-00544-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:aqjoor:v:22:y:2024:i:2:d:10.1007_s10288-023-00544-6

Ordering information: This journal article can be ordered from
https://www.springer ... ch/journal/10288/PSE

DOI: 10.1007/s10288-023-00544-6

Access Statistics for this article

4OR is currently edited by Yves Crama, Michel Grabisch and Silvano Martello

More articles in 4OR from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().