A Flexible Framework for Cubic Regularization Algorithms for Nonconvex Optimization in Function Space

We propose a cubic regularization algorithm that is constructed to deal with nonconvex minimization problems in function space. It allows for a flexible choice of the regularization term and thus accounts for the fact that in such problems one often has to deal with more than one norm. Global and lo...

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Vydáno v:Numerical functional analysis and optimization Ročník 40; číslo 1; s. 85 - 118
Hlavní autor: Schiela, Anton
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 02.01.2019
Taylor & Francis Ltd
Témata:
Algorithms
cubic regularization
Function space
Non-convex optimization
Optimization
optimization in function space
Regularization
Resorts & spas
ISSN:0163-0563, 1532-2467
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Shrnutí:We propose a cubic regularization algorithm that is constructed to deal with nonconvex minimization problems in function space. It allows for a flexible choice of the regularization term and thus accounts for the fact that in such problems one often has to deal with more than one norm. Global and local convergence results are established in a general framework.
Bibliografie:ObjectType-Article-1
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ISSN:0163-0563
1532-2467
DOI:10.1080/01630563.2018.1499114