首页|Improved dynamic regret of distributed online multiple Frank-Wolfe convex optimization
Improved dynamic regret of distributed online multiple Frank-Wolfe convex optimization
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
万方数据
In this paper,we explore a distributed online convex optimization problem over a time-varying multi-agent network.The network aims to minimize a global loss function through local computation and communication with neighboring agents.To effectively handle the optimization problem which involves high-dimensional and structural constraint sets,we develop a distributed online multiple Frank-Wolfe algorithm that circumvents the expensive computational cost associated with projection operations.The dynamic regret bounds are established as O(T1-γ+HT)with the linear oracle number O(T1+γ),which depends on the horizon(total iteration number)T,the function variation HT,and the tuning parameter 0＜γ＜1.In particular,when the prior knowledge of HT and T is available,the bound can be enhanced to O(1+HT).Moreover,we explore the significant advantages provided by the multiple iteration technique and reveal a trade-off between dynamic regret bound,computational cost,and communication cost.Finally,the performance of our algorithm is validated and compared through the distributed online ridge regression problems with two constraint sets.
万方数据
