A Proximal-Proximal Majorization-Minimization Algorithm for Nonconvex Rank Regression Problems

In this paper, we introduce a proximal-proximal majorization-minimization (PPMM) algorithm for nonconvex rank regression problems. The basic idea of the algorithm is to apply the proximal majorization-minimization algorithm to solve the nonconvex problem with the inner subproblems solved by a sparse...

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Vydané v:	IEEE transactions on signal processing Ročník 71; s. 3502 - 3517
Hlavní autori:	Tang, Peipei, Wang, Chengjing, Jiang, Bo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Approximation algorithms Convergence Criteria Linear programming Minimization Nonconvex rank regression problems Optimization proximal point algorithm proximal-proximal majorization-minimization algorithm Regularization semismooth Newton method Signal processing algorithms Urban areas
ISSN:	1053-587X, 1941-0476
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