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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transaction on neural networks and learning systems Ročník 30; číslo 9; s. 2825 - 2839
Hlavní autoři:	Zhang, Hengmin, Qian, Jianjun, Gao, Junbin, Yang, Jian, Xu, Chunyan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.09.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!