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In this paper, we propose a nonmonotone trust region method for bound constrained optimization problems, where the bounds are dealt with by affine scaling technique. Differing from the traditional trust region methods, the subproblem in our algorithm is based on a conic model. Moreover, when the trial point isn’t acceptable by the usual trust region criterion, a line search technique is used to find an acceptable point. This procedure avoids resolving the trust region subproblem, which may reduce the total computational cost. The global convergence and Q-superlinear convergence of the algorithm are established under some mild conditions. Numerical results on a series of standard test problems are reported to show the effectiveness of the new method.
Zhao, Lijuan 1
@article{RO_2019__53_3_787_0,
author = {Zhao, Lijuan},
title = {Nonmonotone conic trust region method with line search technique for bound constrained optimization},
journal = {RAIRO - Operations Research - Recherche Op\'erationnelle},
pages = {787--805},
publisher = {EDP-Sciences},
volume = {53},
number = {3},
year = {2019},
doi = {10.1051/ro/2017054},
mrnumber = {3973144},
zbl = {1461.65192},
language = {en},
url = {http://geodesic.mathdoc.fr/articles/10.1051/ro/2017054/}
}
TY - JOUR AU - Zhao, Lijuan TI - Nonmonotone conic trust region method with line search technique for bound constrained optimization JO - RAIRO - Operations Research - Recherche Opérationnelle PY - 2019 SP - 787 EP - 805 VL - 53 IS - 3 PB - EDP-Sciences UR - http://geodesic.mathdoc.fr/articles/10.1051/ro/2017054/ DO - 10.1051/ro/2017054 LA - en ID - RO_2019__53_3_787_0 ER -
%0 Journal Article %A Zhao, Lijuan %T Nonmonotone conic trust region method with line search technique for bound constrained optimization %J RAIRO - Operations Research - Recherche Opérationnelle %D 2019 %P 787-805 %V 53 %N 3 %I EDP-Sciences %U http://geodesic.mathdoc.fr/articles/10.1051/ro/2017054/ %R 10.1051/ro/2017054 %G en %F RO_2019__53_3_787_0
Zhao, Lijuan. Nonmonotone conic trust region method with line search technique for bound constrained optimization. RAIRO - Operations Research - Recherche Opérationnelle, Tome 53 (2019) no. 3, pp. 787-805. doi : 10.1051/ro/2017054. http://geodesic.mathdoc.fr/articles/10.1051/ro/2017054/
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