Optimization methods for regularization-based ill-posed problems: a survey and a multi-objective framework
type: Item
instance of: scholarly article
title: Optimization methods for regularization-based ill-posed problems: a survey and a multi-objective framework
zbMATH Open document ID: 1405.94024
author: Maoguo Gong
author: Xiangming Jiang
author: Hao Li
published in: Frontiers of Computer Science
publication date: 2019-01-22T00:00:00Z
Mathematics Subject Classification ID: 94A12
Mathematics Subject Classification ID: 90C29
Mathematics Subject Classification ID: 90C59
zbMATH DE Number: 7004884
zbMATH Keywords: ill-posed problem
zbMATH Keywords: regularization
zbMATH Keywords: multi-objective optimization
zbMATH Keywords: evolutionary algorithm
zbMATH Keywords: signal processing
describes a project that uses: PDCO
describes a project that uses: TwIST
describes a project that uses: UTV
describes a project that uses: SPEA2
describes a project that uses: NESTA
describes a project that uses: MOEA/D
describes a project that uses: LSMR
MaRDI profile type: Publication
full work available at URL: s11704-016-5552-0
OpenAlex ID: W2520385699
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Optimization methods for regularization-based ill-posed problems: a survey and a multi-objective framework (DOI)
DOI: 10.1007/S11704-016-5552-0