Online strongly convex optimization with unknown delays

Linear convergence of first order methods for non-strongly convex optimization

Mathematical Programming ◽

10.1007/s10107-018-1232-1 ◽

2018 ◽

Vol 175 (1-2) ◽

pp. 69-107 ◽

Cited By ~ 24

Author(s):

I. Necoara ◽

Yu. Nesterov ◽

F. Glineur

Keyword(s):

Convex Optimization ◽

Linear Convergence ◽

First Order ◽

Strongly Convex ◽

First Order Methods ◽

Adaptive Mirror Descent Algorithms for Convex and Strongly Convex Optimization Problems with Functional Constraints

Journal of Applied and Industrial Mathematics ◽

10.1134/s1990478919030165 ◽

2019 ◽

Vol 13 (3) ◽

pp. 557-574 ◽

Cited By ~ 1

Author(s):

F. S. Stonyakin ◽

M. Alkousa ◽

A. N. Stepanov ◽

A. A. Titov

Keyword(s):

Convex Optimization ◽

Optimization Problems ◽

Functional Constraints ◽

Descent Algorithms ◽

Strongly Convex ◽

Mirror Descent ◽

Adaptive Mirror ◽

Performance of noisy Nesterov's accelerated method for strongly convex optimization problems

2019 American Control Conference (ACC) ◽

10.23919/acc.2019.8814680 ◽

2019 ◽

Cited By ~ 3

Author(s):

Hesameddin Mohammadi ◽

Meisam Razaviyayn ◽

Mihailo R. Jovanovic

Keyword(s):

Convex Optimization ◽

Optimization Problems ◽

Strongly Convex ◽

New results on subgradient methods for strongly convex optimization problems with a unified analysis

Computational Optimization and Applications ◽

10.1007/s10589-016-9841-1 ◽

2016 ◽

Vol 65 (1) ◽

pp. 127-172 ◽

Cited By ~ 1

Author(s):

Masaru Ito

Keyword(s):

Convex Optimization ◽

Optimization Problems ◽

Subgradient Methods ◽

Strongly Convex ◽

A BFGS-IP algorithm for solving strongly convex optimization problems with feasibility enforced by an exact penalty approach

Mathematical Programming ◽

10.1007/s101070100283 ◽

2002 ◽

Vol 92 (3) ◽

pp. 393-424

Author(s):

J. Charles Gilbert ◽

Paul Armand ◽

Sophie Jan-J�gou

Keyword(s):

Convex Optimization ◽

Optimization Problems ◽

Exact Penalty ◽

Strongly Convex ◽

Penalty Approach ◽

Stochastic Subgradient Algorithms for Strongly Convex Optimization Over Distributed Networks

IEEE Transactions on Network Science and Engineering ◽

10.1109/tnse.2017.2713396 ◽

2017 ◽

Vol 4 (4) ◽

pp. 248-260 ◽

Cited By ~ 11

Author(s):

Muhammed O. Sayin ◽

N. Denizcan Vanli ◽

Suleyman S. Kozat ◽

Tamer Basar

Keyword(s):

Convex Optimization ◽

Distributed Networks ◽

Strongly Convex ◽

Subgradient Algorithms ◽

Optimal distributed stochastic mirror descent for strongly convex optimization

Automatica ◽

10.1016/j.automatica.2017.12.053 ◽

2018 ◽

Vol 90 ◽

pp. 196-203 ◽

Cited By ~ 30

Author(s):

Deming Yuan ◽

Yiguang Hong ◽

Daniel W.C. Ho ◽

Guoping Jiang

Keyword(s):

Convex Optimization ◽

Strongly Convex ◽

Mirror Descent ◽

iPiasco: Inertial Proximal Algorithm for Strongly Convex Optimization

Journal of Mathematical Imaging and Vision ◽

10.1007/s10851-015-0565-0 ◽

2015 ◽

Vol 53 (2) ◽

pp. 171-181 ◽

Cited By ~ 36

Author(s):

Peter Ochs ◽

Thomas Brox ◽

Thomas Pock

Keyword(s):

Convex Optimization ◽

Proximal Algorithm ◽

Strongly Convex ◽

Inertial Proximal Algorithm ◽

Accelerated Randomized Mirror Descent Algorithms for Composite Non-strongly Convex Optimization

Journal of Optimization Theory and Applications ◽

10.1007/s10957-018-01469-5 ◽

2019 ◽

Vol 181 (2) ◽

pp. 541-566 ◽

Cited By ~ 1

Author(s):

Le Thi Khanh Hien ◽

Cuong V. Nguyen ◽

Huan Xu ◽

Canyi Lu ◽

Jiashi Feng

Keyword(s):

Convex Optimization ◽

Descent Algorithms ◽

Strongly Convex ◽

Mirror Descent ◽