Quantizer-based distributed mirror descent for multi-agent convex optimization

2021 ◽

pp. 1-1

Author(s):

Robin Brown ◽

Federico Rossi ◽

Kiril Solovey ◽

Matthew Tsao ◽

Michael T. Wolf ◽

...

Keyword(s):

Convex Optimization ◽

Multi Agent Systems ◽

Local Computation ◽

Agent Systems ◽

Structured Convex Optimization ◽

Multi Agent

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Distributed mirror descent method for multi-agent optimization with delay

Neurocomputing ◽

10.1016/j.neucom.2015.12.017 ◽

2016 ◽

Vol 177 ◽

pp. 643-650 ◽

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Author(s):

Jueyou Li ◽

Guo Chen ◽

Zhaoyang Dong ◽

Zhiyou Wu

Keyword(s):

Descent Method ◽

Mirror Descent Method ◽

Mirror Descent ◽

Multi Agent

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Multi-agent reinforcement learning for convex optimization

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III ◽

10.1117/12.2585624 ◽

2021 ◽

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Amir Morcos ◽

Aaron West ◽

Brian Maguire

Keyword(s):

Reinforcement Learning ◽

Convex Optimization ◽

Multi Agent

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Analysis of Newton-Raphson consensus for multi-agent convex optimization under asynchronous and lossy communications

2015 54th IEEE Conference on Decision and Control (CDC) ◽

10.1109/cdc.2015.7402236 ◽

2015 ◽

Cited By ~ 8

Author(s):

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Giuseppe Notarstefano ◽

Luca Schenato ◽

Damiano Varagnolo

Keyword(s):

Convex Optimization ◽

Multi Agent ◽

Newton Raphson

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A proximal center-based decomposition method for multi-agent convex optimization

2008 47th IEEE Conference on Decision and Control ◽

10.1109/cdc.2008.4738764 ◽

2008 ◽

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Ion Necoara ◽

Johan A.K. Suykens

Keyword(s):

Convex Optimization ◽

Decomposition Method ◽

Multi Agent

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“Relative Continuity” for Non-Lipschitz Nonsmooth Convex Optimization Using Stochastic (or Deterministic) Mirror Descent

INFORMS Journal on Optimization ◽

10.1287/ijoo.2018.0008 ◽

2019 ◽

Vol 1 (4) ◽

pp. 288-303 ◽

Cited By ~ 1

Author(s):

Haihao Lu

Keyword(s):

Convex Optimization ◽

Nonsmooth Convex Optimization ◽

Mirror Descent

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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 ◽

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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 ◽

Convex Optimization Problems ◽

Mirror Descent ◽

Adaptive Mirror ◽

Strongly Convex Optimization

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Stochastic mirror descent method for distributed multi-agent optimization

Optimization Letters ◽

10.1007/s11590-016-1071-z ◽

2016 ◽

Vol 12 (6) ◽

pp. 1179-1197 ◽

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Author(s):

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Guoquan Li ◽

Zhiyou Wu ◽

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Keyword(s):

Descent Method ◽

Mirror Descent Method ◽

Mirror Descent ◽

Multi Agent

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An approximate dual subgradient algorithm for multi-agent non-convex optimization

49th IEEE Conference on Decision and Control (CDC) ◽

10.1109/cdc.2010.5717220 ◽

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Sonia Martinez

Keyword(s):

Convex Optimization ◽

Multi Agent ◽

Subgradient Algorithm

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On decentralized convex optimization in a multi-agent setting with separable constraints and its application to optimal charging of electric vehicles

2016 IEEE 55th Conference on Decision and Control (CDC) ◽

10.1109/cdc.2016.7799197 ◽

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Kostas Margellos ◽

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Convex Optimization ◽

Electric Vehicles ◽

Multi Agent

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Quantizer-based distributed mirror descent for multi-agent convex optimization

On Local Computation for Network-Structured Convex Optimization in Multi-Agent Systems

Distributed mirror descent method for multi-agent optimization with delay

Multi-agent reinforcement learning for convex optimization

Analysis of Newton-Raphson consensus for multi-agent convex optimization under asynchronous and lossy communications

A proximal center-based decomposition method for multi-agent convex optimization

“Relative Continuity” for Non-Lipschitz Nonsmooth Convex Optimization Using Stochastic (or Deterministic) Mirror Descent

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

Stochastic mirror descent method for distributed multi-agent optimization

An approximate dual subgradient algorithm for multi-agent non-convex optimization

On decentralized convex optimization in a multi-agent setting with separable constraints and its application to optimal charging of electric vehicles

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