Convergence of memory gradient methods

Authors:
Zhen-Jun Shi;Jinhua Guo
Affiliations:
College of Operations Research and Management, Qufu Normal University, Rizhao, Shandong, China,Department of Computer & Information Science, University of Michigan, Dearborn, MI, USA;Department of Computer & Information Science, University of Michigan, Dearborn, MI, USA
Venue:
International Journal of Computer Mathematics
Year:
2008

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Abstract

In this paper we present a new class of memory gradient methods for unconstrained optimization problems and develop some useful global convergence properties under some mild conditions. In the new algorithms, trust region approach is used to guarantee the global convergence. Numerical results show that some memory gradient methods are stable and efficient in practical computation. In particular, some memory gradient methods can be reduced to the BB method in some special cases.