Projected Adaptive Cubic Regularization Algorithm with Derivative-Free Filter Technique for Box Constrained Optimization

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 appro...

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Main Authors: Lingyun He, Peng Wang, Detong Zhu
Format: Article
Language:English
Published: Wiley 2021-01-01
Series:Discrete Dynamics in Nature and Society
Online Access:http://dx.doi.org/10.1155/2021/1496048
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author Lingyun He
Peng Wang
Detong Zhu
author_facet Lingyun He
Peng Wang
Detong Zhu
author_sort Lingyun He
collection DOAJ
description 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.
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institution Kabale University
issn 1607-887X
language English
publishDate 2021-01-01
publisher Wiley
record_format Article
series Discrete Dynamics in Nature and Society
spelling doaj-art-7a93f4f648474efcab4c0ae166accbf12025-02-03T06:06:31ZengWileyDiscrete Dynamics in Nature and Society1607-887X2021-01-01202110.1155/2021/1496048Projected Adaptive Cubic Regularization Algorithm with Derivative-Free Filter Technique for Box Constrained OptimizationLingyun He0Peng Wang1Detong Zhu2Mathematics and Science CollegeMathematics and Statistics CollegeMathematics and Science CollegeAn 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.http://dx.doi.org/10.1155/2021/1496048
spellingShingle Lingyun He
Peng Wang
Detong Zhu
Projected Adaptive Cubic Regularization Algorithm with Derivative-Free Filter Technique for Box Constrained Optimization
Discrete Dynamics in Nature and Society
title Projected Adaptive Cubic Regularization Algorithm with Derivative-Free Filter Technique for Box Constrained Optimization
title_full Projected Adaptive Cubic Regularization Algorithm with Derivative-Free Filter Technique for Box Constrained Optimization
title_fullStr Projected Adaptive Cubic Regularization Algorithm with Derivative-Free Filter Technique for Box Constrained Optimization
title_full_unstemmed Projected Adaptive Cubic Regularization Algorithm with Derivative-Free Filter Technique for Box Constrained Optimization
title_short Projected Adaptive Cubic Regularization Algorithm with Derivative-Free Filter Technique for Box Constrained Optimization
title_sort projected adaptive cubic regularization algorithm with derivative free filter technique for box constrained optimization
url http://dx.doi.org/10.1155/2021/1496048
work_keys_str_mv AT lingyunhe projectedadaptivecubicregularizationalgorithmwithderivativefreefiltertechniqueforboxconstrainedoptimization
AT pengwang projectedadaptivecubicregularizationalgorithmwithderivativefreefiltertechniqueforboxconstrainedoptimization
AT detongzhu projectedadaptivecubicregularizationalgorithmwithderivativefreefiltertechniqueforboxconstrainedoptimization