Projected adaptive cubic regularization algorithm with derivative-free filter technique for box constrained optimization
type: Item
instance of: scholarly article
title: Projected adaptive cubic regularization algorithm with derivative-free filter technique for box constrained optimization
zbMATH Open document ID: 1486.90187
DOI: 10.1155/2021/1496048
author: Lingyun He
author: Peng Wang
author: Detong Zhu
published in: Discrete Dynamics in Nature and Society
publication date: 2021-11-12T00:00:00Z
review text: Summary: An adaptive projected affine scaling algorithm of cubic regularization method using a filter technique for solving box constrained optimization without derivatives is put forward in the passage. The affine scaling interior-point cubic model is based on the quadratic probabilistic interpolation approach on the objective function. The new iterations are obtained by the solutions of the projected adaptive cubic regularization algorithm with filter technique. We prove the convergence of the proposed algorithm under some assumptions. Finally, experiments results showed that the presented algorithm is effective in detail.
Mathematics Subject Classification ID: 90C30
Mathematics Subject Classification ID: 65K05
Mathematics Subject Classification ID: 90C56
zbMATH DE Number: 7425166
describes a project that uses: NEWUOA
describes a project that uses: BOBYQA
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2021/1496048
OpenAlex ID: W3209962261
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description: scientific article