1. Convergence of memory gradient methods.
- Author
-
Shi, Zhen-Jun and Guo, Jinhua
- Subjects
STOCHASTIC convergence ,MATHEMATICAL optimization ,ALGORITHMS ,MATHEMATICAL analysis ,MATHEMATICS - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2008
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