A sufficient descent three-term conjugate gradient method via symmetric rank-one update for large-scale optimization. (2nd January 2016)

• Record Type: Journal Article
• Authors: Moyi, Aliyu Usman; Leong, Wah June
• Is Part Of: Optimization. Volume 65:Number 1(2016)
• Page Start: 121
• Page End: 143
• Publication Date: 2016-01-02
• Subjects: unconstrained optimization -- symmetric rank-one update -- three-term conjugate gradient method -- memoryless update -- sufficient descent; 65K10 -- 90C06 -- 90C53; Mathematical optimization -- Periodicals; 519.7
• DOI: 10.1080/02331934.2014.994625
• Languages: English
• ISSNs: 0233-1934

Abstract

Abstract : In this paper, we propose a three-term conjugate gradient method via the symmetric rank-one update. The basic idea is to exploit the good properties of the SR1 update in providing quality Hessian approximations to construct a conjugate gradient line search direction without the storage of matrices and possess the sufficient descent property. Numerical experiments on a set of standard unconstrained optimization problems showed that the proposed method is superior to many well-known conjugate gradient methods in terms of efficiency and robustness.