.png){kind=icon}
返回
Distributed Gradient Tracking for Unbalanced Optimization With Different Constraint Sets
.png){kind=icon}
PRE
AI
DOI：10.1109/TAC.2022.3192316.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
tracking methods have become popular for distributed optimization in recent years, partially because they achieve linear convergence using only a constant step-size for strongly convex optimization. In this article, we construct a counterexample on constrained optimization to show that direct extension of gradient tracking by using projections cannot guarantee the correctness. Then, we propose projected gradient tracking algorithms with diminishing step-sizes rather than a constant one for distributed strongly convex optimization with different constraint sets and unbalanced graphs. Our basic algorithm can achieve O(ln T/T ) convergence rate. Moreover, we design an epoch iteration scheme and improve the convergence rate as O(1/T ).
Keyword:
Optimization
Convergence
Directed graphs
Convex functions
Multi-agent systems
Linear programming
Heuristic algorithms
different constraint sets
distrib- uted optimization
gradient tracking
unbalanced graphs
期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容