An improved Dai–Kou conjugate gradient algorithm for unconstrained optimization

It is gradually accepted that the loss of orthogonality of the gradients in a conjugate gradient algorithm may decelerate the convergence rate to some extent. The Dai–Kou conjugate gradient algorithm (SIAM J Optim 23(1):296–320, 2013), called CGOPT, has attracted many researchers’ attentions due to...

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Veröffentlicht in:	Computational optimization and applications Jg. 75; H. 1; S. 145 - 167
Hauptverfasser:	Liu, Zexian, Liu, Hongwei, Dai, Yu-Hong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.01.2020 Springer Nature B.V
Schlagworte:	Algorithms Conjugates Convergence Convex and Discrete Geometry Deceleration Design modifications Distributed processing Management Science Mathematics Mathematics and Statistics Multiprocessing Numerical methods Operations Research Operations Research/Decision Theory Optimization Orthogonality Quasi Newton methods Queuing theory Statistics Global convergence Quasi-Newton method Preconditioned conjugate gradient algorithm 90C06 90C26 65Y20 Limited memory Conjugate gradient algorithm
ISSN:	0926-6003, 1573-2894
Online-Zugang:	Volltext
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