Global convergence of a modified Hestenes-Stiefel nonlinear conjugate gradient method with Armijo line search

Authors:
Zhifeng Dai;Fenghua Wen
Affiliations:
College of Mathematics and Computational Science, Changsha University of Science and Technology, Changsha, China;School of Econometrics and Management, Changsha University of Science and Technology, Changsha, China
Venue:
Numerical Algorithms
Year:
2012

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Abstract

In this article, based on the modified secant equation, we propose a modified Hestenes-Stiefel (HS) conjugate gradient method which has similar form as the CG-DESCENT method proposed by Hager and Zhang (SIAM J Optim 16:170---192, 2005). The presented method can generate sufficient descent directions without any line search. Under some mild conditions, we show that it is globally convergent with Armijo line search. Moreover, the R-linear convergence rate of the modified HS method is established. Preliminary numerical results show that the proposed method is promising, and competitive with the well-known CG-DESCENT method.