Dogleg paths and trust region methods with back tracking technique for unconstrained optimization

Authors:
Cheng-jing Wang
Affiliations:
Department of Mathematics, Zhejiang University, Hangzhou, PR China
Venue:
Applied Mathematics and Computation
Year:
2006

Citing 5
Cited 0

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A Class of Indefinite Dogleg Path Methods for Unconstrained Minimization

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Abstract

In this paper, we improve approximate trust region methods via a class of dogleg paths for unconstrained optimization. The dogleg paths include both definite and indefinite ones. A hybrid strategy using both trust region and line search techniques is adopted which switches to back tracking steps when a trial step produced by the trust region subproblem is unacceptable. We show that the algorithm preserves the strong convergence properties of trust region methods. Numerical results are presented and discussed.