Review paper

Perturbed proximal primal–dual algorithm for nonconvex nonsmooth optimization

Volume: 176, Issue: 1-2, Pages: 207 - 245
Published: Feb 20, 2019
Abstract
In this paper, we propose a perturbed proximal primal–dual algorithm (PProx-PDA) for an important class of linearly constrained optimization problems, whose objective is the sum of smooth (possibly nonconvex) and convex (possibly nonsmooth) functions. This family of problems can be used to model many statistical and engineering applications, such as high-dimensional subspace estimation and the distributed machine learning. The proposed method is...
Paper Details
Title
Perturbed proximal primal–dual algorithm for nonconvex nonsmooth optimization
Published Date
Feb 20, 2019
Volume
176
Issue
1-2
Pages
207 - 245
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