Event-Triggered Distributed Stochastic Mirror Descent for Convex Optimization

2022 ◽  
pp. 1-12
Author(s):  
Menghui Xiong ◽  
Baoyong Zhang ◽  
Daniel W. C. Ho ◽  
Deming Yuan ◽  
Shengyuan Xu
Keyword(s):  
Convex Optimization ◽  
Mirror Descent ◽  
Event Triggered

scholarly journals Distributed Event-Triggered Subgradient Method for Convex Optimization with a Common Constraint Set

2017 ◽  
Vol 50 (1) ◽  
pp. 15319-15324 ◽  
Author(s):  
Yuichi Kajiyama ◽  
Naoki Hayashi ◽  
Shigemasa Takai
Keyword(s):  
Convex Optimization ◽  
Subgradient Method ◽  
Constraint Set ◽  
Event Triggered

Algorithms for Convex Optimization

2021 ◽  
Author(s):  
Nisheeth K. Vishnoi
Keyword(s):  
Convex Optimization ◽  
Data Science ◽  
Optimization Problems ◽  
Algorithm Design ◽  
Continuous Optimization ◽  
Maximum Flow ◽  
Maximum Matching ◽  
Science Data ◽  
Mirror Descent ◽  
Continuous Optimization Problems

In the last few years, Algorithms for Convex Optimization have revolutionized algorithm design, both for discrete and continuous optimization problems. For problems like maximum flow, maximum matching, and submodular function minimization, the fastest algorithms involve essential methods such as gradient descent, mirror descent, interior point methods, and ellipsoid methods. The goal of this self-contained book is to enable researchers and professionals in computer science, data science, and machine learning to gain an in-depth understanding of these algorithms. The text emphasizes how to derive key algorithms for convex optimization from first principles and how to establish precise running time bounds. This modern text explains the success of these algorithms in problems of discrete optimization, as well as how these methods have significantly pushed the state of the art of convex optimization itself.

scholarly journals Optimal distributed stochastic mirror descent for strongly convex optimization

Automatica ◽  
2018 ◽  
Vol 90 ◽  
pp. 196-203 ◽  
Author(s):  
Deming Yuan ◽  
Yiguang Hong ◽  
Daniel W.C. Ho ◽  
Guoping Jiang
Keyword(s):  
Convex Optimization ◽  
Strongly Convex ◽  
Mirror Descent ◽  
Strongly Convex Optimization

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

2019 ◽  
Vol 181 (2) ◽  
pp. 541-566 ◽  
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 ◽  
Strongly Convex Optimization
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