-

 

 

 




Optimization Online





 

A globally and linearly convergent PGM for zero-norm regularized quadratic optimization with sphere constraint

Wu Yuqia(935897967***at***qq.com)
Pan Shaohua(shhpan***at***scut.edu.cn)
Bi Shujun(bishj***at***scut.edu.cn)

Abstract: This paper is concerned with the zero-norm regularized quadratic optimization with a sphere constraint, which has an important application in sparse eigenvalue problems. For this class of nonconvex and nonsmooth optimization problems, we establish the KL property of exponent 1/2 for its extended-valued objective function and develop a globally and linearly convergent proximal gradient method (PGM). Numerical experiments are included for sparse principal component analysis (PCA) with synthetic and real data to confirm the obtained theoretic results.

Keywords: KL property of exponent $1/2$; zero-norm; sphere constraint; PGM

Category 1: Nonlinear Optimization (Bound-constrained Optimization )

Citation:

Download: [PDF]

Entry Submitted: 11/11/2018
Entry Accepted: 11/11/2018
Entry Last Modified: 11/11/2018

Modify/Update this entry


  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository

 

Submit
Update
Policies
Coordinator's Board
Classification Scheme
Credits
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Optimization Society