Monday, 15:15 - 15:40 h, Room: H 0112

 

Saman Babaie-Kafaki
A modification on the Hager-Zhang conjugate gradient method

 

Abstract:
Conjugate gradient (CG) methods comprise a class of unconstrained optimization algorithms
characterized by low memory requirements and strong global convergence properties which made them
popular for engineers and mathematicians engaged in solving large-scale unconstrained optimization
problems. One of the efficient CG methods has been proposed by Hager and Zhang. Here, a singular
value study is made in order to find lower and upper bounds for the condition number of the matrix
which generates the search directions of the Hager-Zhang method. Then, based on the insight gained
by our analysis, a modified version of the Hager-Zhang method is proposed using an adaptive switch
form the Hager-Zhang method to the Hestenes-Stiefel method, when the mentioned condition number is
large. It can be shown that if the line search fulfills the strong Wolfe conditions, then the
proposed method is globally convergent for uniformly convex objective functions. Numerical
experiments on a set of unconstrained optimization test problems of the CUTEr collection demonstrate
the efficiency of the suggested adaptive CG method in the sense of the performance profile
introduced by Dolan and Moré.

 

Talk 1 of the contributed session Mon.3.H 0112
"Unconstrained optimization I" [...]
Cluster 16
"Nonlinear programming" [...]

 

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