Frank-Wolfe(FW)method has well-documented merits in handling convex constraints,but existing stochastic FW algorithms are basically developed for centralized settings.In this context,the present work puts forth a distributed stochastic Frank-Wolfe solver,by judiciously combining Nesterov's momentum and ...
1 provides a comparison of the algorithms proposed in the context. the following is the main contributions of our work. we put forth a distributed stochastic zeroth-order frank-wolfe algorithm (dszo-fw) by using the gradient tracking technique, the momentum-based variance reduction technique, and...
A distributed Frank–Wolfe framework for learning low-rank matrices with the trace norm Article 10 May 2018 1 Introduction Social network analysis has received much research interest in the past decade. A social network can be regarded as a directed graph, where nodes represent users and weight...
We provide an extensivetheoretical study and experimentally validate the performance of our algorithms by comparing themwith existing ones on real-world problems.Keywords Online Learning · Distributed Learning · Delayed Feedback · Frank-Wolfe Algorithm1 IntroductionMany machine learning (ML) applications...
To this end, we propose a distributed Frank-Wolfe (dFW) algorithm. We obtain theoretical guarantees on the optimization error 蔚 and communication cost that do not depend on the total number of combining elements. We further show that the communication cost of dFW is optimal by deriving a ...
FRANK-WOLFE ALGORITHMCONVEXGAMESIn this paper, we focus on solving a distributed convex aggregative optimization problem in a network, where each agent has its own cost function which depends not only on its own decision variables but also on the aggregated function of all agents' decision ...
The upper layer model predictive control is identical for all RUs, while in a lower layer a mixed-integer optimization using the decentralized Frank-Wolfe algorithm secures optimal switching of the RUs. The operational behavior can be easily and transparently adjusted in a wide range. Furthermore,...
Veeranjaneyulu SadhanalaWei DaiWillie NeiswangerYu-Xiang WangWang, Y.-X., Sadhanala, V., Dai, W., Neiswanger, W., Sra, S., and Xing, E. P. Parallel and distributed block-coordinate Frank-Wolfe algorithms. arXiv:1409.6086v2, 2014....
A Distributed Frank-Wolfe Framework for Learning Low-Rank Matrices with the Trace Normdoi:10.1007/S10994-018-5713-5Wenjie ZhengAurélien BelletPatrick GallinariSpringer US
Distributed computation 12 dynamic traffic equilibria - Wisten, Smith - 1997 () Citation Context ...ds to compute the user optimum defined by Wardrop’s principle such as, e.g., the Frank-Wolfe algorithm [24], the method of the successive averages [19], feedback strategies [23], ...