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Distributed Optimization

The goal of Distributed Optimization is to optimize a certain objective defined over millions of billions of data that is distributed over many machines by utilizing the computational power of these machines.

Source: Analysis of Distributed StochasticDual Coordinate Ascent

Papers

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Showing 251–260 of 536 papers

TitleStatusHype
Distributed gradient-based optimization in the presence of dependent aperiodic communication—0
Distributed Learning of Generalized Linear Causal Networks—0
Coordinated Day-ahead Dispatch of Multiple Power Distribution Grids hosting Stochastic Resources: An ADMM-based Framework—0
Distributed Random Reshuffling over Networks—0
Convergence Rates of Two-Time-Scale Gradient Descent-Ascent Dynamics for Solving Nonconvex Min-Max Problems—0
Communication-Efficient Distributed SGD with Compressed Sensing—0
Distributed Graph Learning with Smooth Data Priors—0
Collaborative Learning over Wireless Networks: An Introductory Overview—0
Variance Reduction in Deep Learning: More Momentum is All You Need—0
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning—0
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