A Class of Indefinite Dogleg Path Methods for Unconstrained Minimization

Authors:
Jianzhong Zhang;Chengxian Xu
Affiliations:
-;-
Venue:
SIAM Journal on Optimization
Year:
1999

Citing 0
Cited 5

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A new type of condensation curvilinear path algorithm for unconstrained generalized geometric programming

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Abstract

In this paper we propose a convenient curvilinear search method to solve trust region problems arising from unconstrained optimization problems. The curvilinear paths we set forth are dogleg paths, generated mainly by employing Bunch--Parlett factorization for general symmetric matrices that may be indefinite. This method is easy to implement and globally convergent. It is proved that the method satisfies the first- and second-order stationary point convergence properties and that the convergence rate is quadratic under commonly used conditions on functions. Numerical experiments are conducted to compare this method with some existing methods.