Global convergence of a new sufficient descent spectral three-term conjugate gradient class for large-scale optimization. (4th May 2022)

• Record Type: Journal Article
• Authors: Eslahchi, M. R.; Bojari, S.
• Is Part Of: Optimization methods and software. Volume 37:Number 3(2022)
• Page Start: 830
• Page End: 843
• Publication Date: 2022-05-04
• Subjects: Large scale unconstrained optimization -- conjugate gradient method -- global convergence; 90C30 -- 90C06 -- 90C26; Mathematical optimization -- Periodicals; Algorithms -- Periodicals; 519.7
• DOI: 10.1080/10556788.2020.1843167
• Languages: English
• ISSNs: 1055-6788

Abstract

ABSTRACT: To solve a large-scale unconstrained optimization problem, in this paper we propose a class of spectral three-term conjugate gradient methods. We indicate that the proposed class, in fact, generates sufficient descent directions and also fulfill Dai–Liao conjugacy condition. We prove the global convergence of the presented class for either uniformly convex or general smooth functions under some suitable conditions, in detail. Finally, in a set of numerical experiments which contains eight conjugate gradient methods and 260 standard problems, we illustrate the efficiency and effectiveness of our class.