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Volume 13, Issue 1
A Nonmonotonic Trust Region Technique for Nonlinear Constrained Optimization

De-Tong Zhu

J. Comp. Math., 13 (1995), pp. 20-31.

Published online: 1995-02

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  • Abstract

In this paper, a nonmonotonic trust region method for optimization problems with equality constraints is proposed by introducing a nonsmooth merit function and adopting a correction step. It is proved that all accumulation points of the iterates generated by the proposed algorithm are Kuhn-Tucker points and that the algorithm is $q$-superlinearly convergent.

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@Article{JCM-13-20, author = {Zhu , De-Tong}, title = {A Nonmonotonic Trust Region Technique for Nonlinear Constrained Optimization}, journal = {Journal of Computational Mathematics}, year = {1995}, volume = {13}, number = {1}, pages = {20--31}, abstract = {

In this paper, a nonmonotonic trust region method for optimization problems with equality constraints is proposed by introducing a nonsmooth merit function and adopting a correction step. It is proved that all accumulation points of the iterates generated by the proposed algorithm are Kuhn-Tucker points and that the algorithm is $q$-superlinearly convergent.

}, issn = {1991-7139}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jcm/9248.html} }
TY - JOUR T1 - A Nonmonotonic Trust Region Technique for Nonlinear Constrained Optimization AU - Zhu , De-Tong JO - Journal of Computational Mathematics VL - 1 SP - 20 EP - 31 PY - 1995 DA - 1995/02 SN - 13 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jcm/9248.html KW - AB -

In this paper, a nonmonotonic trust region method for optimization problems with equality constraints is proposed by introducing a nonsmooth merit function and adopting a correction step. It is proved that all accumulation points of the iterates generated by the proposed algorithm are Kuhn-Tucker points and that the algorithm is $q$-superlinearly convergent.

Zhu , De-Tong. (1995). A Nonmonotonic Trust Region Technique for Nonlinear Constrained Optimization. Journal of Computational Mathematics. 13 (1). 20-31. doi:
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