Two accelerated nonmonotone adaptive trust region line search methods

Babaie-Kafaki, Saman; Rezaee, Saeed

doi:10.1007/s11075-017-0406-x

Cited by 8 publications

(8 citation statements)

References 19 publications

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“…To describe our randomization scheme, we use the framework of the TR-LS algorithm proposed in [2]. Firstly, we adopt the adaptive choice of the TR radius suggested in [5], that is…”

Section: A Randomized Trust Region Line Search Algorithmmentioning

confidence: 99%

“…Otherwise, to avoid resolving the TR subproblem (1), we set x k+1 = x k + s k with a specific probability which depends on the value of ρ k , or (similar to the approach of [2]) we use the Armijo-type LS procedure proposed by Wan et al [6] as follows:…”

Section: A Randomized Trust Region Line Search Algorithmmentioning

confidence: 99%

“…It is worth noting that to decrease computational cost of the TR methods, the LS techniques can be effectively employed in the case where the TR ratio is small, as an alternative of resolving the approximate model in a reduced neighborhood. A review of the literature reveals an abundance of the studies on the TR methods; see for example [2][3][4] and the references therein.…”

Section: Introductionmentioning

confidence: 99%

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A randomized adaptive trust region line search method

Babaie-Kafaki

Rezaee²

2020

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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“…To describe our randomization scheme, we use the framework of the TR-LS algorithm proposed in [2]. Firstly, we adopt the adaptive choice of the TR radius suggested in [5], that is…”

Section: A Randomized Trust Region Line Search Algorithmmentioning

confidence: 99%

Section: A Randomized Trust Region Line Search Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A randomized adaptive trust region line search method

Babaie-Kafaki

Rezaee²

2020

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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“…In [28], the hybridization of monotone and non-monotone approaches is made; a modified trust region ratio is used, in which more information is provided about the agreement between the exact and the approximate models. An adaptive trust region radius is used, as well as two accelerated Armijo-type line search strategies to avoid resolving the trust region subproblem whenever a trial step is rejected.…”

Section: The Computational Time Is Reducedmentioning

confidence: 99%

Unconstrained Optimization Methods: Conjugate Gradient Methods and Trust-Region Methods

Djordjević¹

2019

Applied Mathematics

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Here, we consider two important classes of unconstrained optimization methods: conjugate gradient methods and trust region methods. These two classes of methods are very interesting; it seems that they are never out of date. First, we consider conjugate gradient methods. We also illustrate the practical behavior of some conjugate gradient methods. Then, we study trust region methods. Considering these two classes of methods, we analyze some recent results.

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“…More recently, Peyghami and Ataee Tarzanagh (2015) suggested a truncated version of (7). Also, based on an eigenvalue analysis carried out on a memoryless version of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) update, Rezaee and Babaie-Kafaki (2019) suggested another adaptive TR radius (see also Babaie-Kafaki and Rezaee, 2018).…”

Section: Introductionmentioning

confidence: 99%

A randomized nonmonotone adaptive trust region method based on the simulated annealing strategy for unconstrained optimization

Babaie-Kafaki

Rezaee

2019

IJICC

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Purpose The purpose of this paper is to employ stochastic techniques to increase efficiency of the classical algorithms for solving nonlinear optimization problems. Design/methodology/approach The well-known simulated annealing strategy is employed to search successive neighborhoods of the classical trust region (TR) algorithm. Findings An adaptive formula for computing the TR radius is suggested based on an eigenvalue analysis conducted on the memoryless Broyden-Fletcher-Goldfarb-Shanno updating formula. Also, a (heuristic) randomized adaptive TR algorithm is developed for solving unconstrained optimization problems. Results of computational experiments on a set of CUTEr test problems show that the proposed randomization scheme can enhance efficiency of the TR methods. Practical implications The algorithm can be effectively used for solving the optimization problems which appear in engineering, economics, management, industry and other areas. Originality/value The proposed randomization scheme improves computational costs of the classical TR algorithm. Especially, the suggested algorithm avoids resolving the TR subproblems for many times.

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Two accelerated nonmonotone adaptive trust region line search methods

Cited by 8 publications

References 19 publications

A randomized adaptive trust region line search method

A randomized adaptive trust region line search method

Unconstrained Optimization Methods: Conjugate Gradient Methods and Trust-Region Methods

A randomized nonmonotone adaptive trust region method based on the simulated annealing strategy for unconstrained optimization

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