Practical proximal primal-dual algorithms for structured saddle point problems Practical proximal primal-dual algorithms for structured saddle point problems

In this paper, we are concerned with a class of convex-concave saddle point problems, where one of the objective parts is assumed to be a convex and smooth function with Lipschitz continuous gradient. By exploiting the bilinear structure of the objective, we first propose a practical accelerated Pro...

Full description

Saved in:

Bibliographic Details
Published in:	Journal of global optimization Vol. 93; no. 3; pp. 803 - 831
Main Authors:	Qu, Yunfei, He, Hongjin, Zhang, Tao, Han, Deren
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.11.2025 Springer Nature B.V
Subjects:	Algorithms Computer Science Convex analysis Euclidean space Lipschitz condition Mathematics Mathematics and Statistics Operations Research/Decision Theory Optimization Real Functions Saddle points restarting technique Primary 65K05 saddle point problem inexact criterion Primal-dual algorithm 90C25 65K10 accelerated algorithm
ISSN:	0925-5001, 1573-2916
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!