An adaptive nonmonotone trust region method based on a modified scalar approximation of the Hessian in the successive quadratic subproblems
RAIRO - Operations Research - Recherche Opérationnelle, Tome 53 (2019) no. 3, pp. 829-839.

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.

DOI : 10.1051/ro/2017057
Classification : 65K05, 90C53, 49M37
Mots-clés : Unconstrained optimization, trust region method, secant equation, adaptive radius, global convergence, superlinear convergence
Rezaee, Saeed 1 ; Babaie-Kafaki, Saman 1

1
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     author = {Rezaee, Saeed and Babaie-Kafaki, Saman},
     title = {An adaptive nonmonotone trust region method based on a modified scalar approximation of the {Hessian} in the successive quadratic subproblems},
     journal = {RAIRO - Operations Research - Recherche Op\'erationnelle},
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     publisher = {EDP-Sciences},
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Rezaee, Saeed; Babaie-Kafaki, Saman. An adaptive nonmonotone trust region method based on a modified scalar approximation of the Hessian in the successive quadratic subproblems. RAIRO - Operations Research - Recherche Opérationnelle, Tome 53 (2019) no. 3, pp. 829-839. doi : 10.1051/ro/2017057. http://www.numdam.org/articles/10.1051/ro/2017057/

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