Scalable Proximal Jacobian Iteration Method With Global Convergence Analysis for Nonconvex Unconstrained Composite Optimizations

The recent studies have found that the nonconvex relaxation functions usually perform better than the convex counterparts in the <inline-formula> <tex-math notation="LaTeX">l_{0} </tex-math></inline-formula>-norm and rank function minimization problems. However, due...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 30; H. 9; S. 2825 - 2839
Hauptverfasser:	Zhang, Hengmin, Qian, Jianjun, Gao, Junbin, Yang, Jian, Xu, Chunyan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.09.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Acceleration Algorithms Basic converters Computer applications Convergence Convexity Critical point Generalized proximal thresholding operators global convergence analysis Iterative methods Jacobian matrices kurdyka–Łojasiewica (KŁ) property Learning systems Linear programming Minimization nonconvex sparse and low-rank optimization Nonlinear programming Objective function Optimization proximal Jacobian iteration method (PJIM)
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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