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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 341–350 of 536 papers

TitleStatusHype
Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies—0
Byzantine-Robust Learning on Heterogeneous Datasets via Resampling—0
Asynchronous Distributed Optimization with Stochastic Delays—0
Distributed Mirror Descent with Integral Feedback: Asymptotic Convergence Analysis of Continuous-time Dynamics—0
Graph neural networks-based Scheduler for Production planning problems using Reinforcement Learning—0
Distributed Optimization, Averaging via ADMM, and Network TopologyCode0
On Communication Compression for Distributed Optimization on Heterogeneous Data—0
GTAdam: Gradient Tracking with Adaptive Momentum for Distributed Online Optimization—0
Projected Push-Sum Gradient Descent-Ascent for Convex Optimizationwith Application to Economic Dispatch Problems—0
Distributed Personalized Gradient Tracking with Convex Parametric Models—0
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