New Bregman proximal type algorithms for solving DC optimization problems

Difference of Convex (DC) optimization problems have objective functions that are differences between two convex functions. Representative ways of solving these problems are the proximal DC algorithms, which require that the convex part of the objective function have L -smoothness. In this article,...

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Veröffentlicht in:	Computational optimization and applications Jg. 83; H. 3; S. 893 - 931
Hauptverfasser:	Takahashi, Shota, Fukuda, Mituhiro, Tanaka, Mirai
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.12.2022 Springer Nature B.V
Schlagworte:	Algorithms Convex and Discrete Geometry Critical point Management Science Mathematics Mathematics and Statistics Operations Research Operations Research/Decision Theory Optimization Phase retrieval Smoothness Statistics Kurdyka-Łojasiewicz inequality Nonconvex optimization 90C30 65K05 Nonsmooth optimization 90C26 Bregman proximal DC algorithms Difference-of-convex optimization Bregman distances
ISSN:	0926-6003, 1573-2894
Online-Zugang:	Volltext
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