Convergence of supermemory gradient method

Authors:
Zhen-Jun Shi;Jie Shen
Affiliations:
College of Operations Reaserch and Management, Qufu Normal University, Rizhao, Shan-dong, P. R. China and Department of Computer & Information Science, University of Michigan-Dearborn, Michigan;Department of Computer & Information Science, University of Michigan-Dearborn, Michigan
Venue:
Journal of Applied Mathematics and Computing
Year:
2007

Citing 6
Cited 0

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Abstract

In this paper we consider the global convergence of a new supermemory gradient method for unconstrained optimization problems. New trust region radius is proposed to make the new method converge stably and averagely, and it will be suitable to solve large scale minimization problems. Some global convergence results are obtained under some mild conditions. Numerical results show that this new method is effective and stable in practical computation.