Volume 53, Number 3, July-September 2019
|Page(s)||829 - 839|
|Published online||25 June 2019|
An adaptive nonmonotone trust region method based on a modified scalar approximation of the Hessian in the successive quadratic subproblems
Department of Mathematics, Faculty of Mathematics, Statistics and Computer Science, Semnan University, P.O. Box: 35195–363, Semnan, Iran
* Corresponding author: firstname.lastname@example.org
Accepted: 23 July 2017
Based on a modified secant equation, we propose a scalar approximation of the Hessian to be used in the trust region subproblem. Then, we suggest an adaptive nonmonotone trust region algorithm with a simple quadratic model. Under proper conditions, it is briefly shown that the proposed algorithm is globally and locally superlinearly convergent. Numerical experiments are done on a set of unconstrained optimization test problems of the CUTEr collection, using the Dolan-Moré performance profile. They demonstrate efficiency of the proposed algorithm.
Mathematics Subject Classification: 65K05 / 90C53 / 49M37
Key words: Unconstrained optimization / trust region method / secant equation / adaptive radius / global convergence / superlinear convergence
© EDP Sciences, ROADEF, SMAI 2019
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