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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 211–220 of 536 papers

TitleStatusHype
Distributed Online Optimization in Dynamic Environments Using Mirror Descent—0
GTAdam: Gradient Tracking with Adaptive Momentum for Distributed Online Optimization—0
Distributed Online Optimization with Byzantine Adversarial Agents—0
Communication Efficient, Differentially Private Distributed Optimization using Correlation-Aware Sketching—0
Distributed Optimization for Client-Server Architecture with Negative Gradient Weights—0
Distributed Optimization for Massive Connectivity—0
A Provably Communication-Efficient Asynchronous Distributed Inference Method for Convex and Nonconvex Problems—0
Distributed Optimization for Over-Parameterized Learning—0
Correlated quantization for distributed mean estimation and optimization—0
Asynchronous Distributed Optimization with Delay-free Parameters—0
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