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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 141–150 of 536 papers

TitleStatusHype
Asynchronous Forward Bounding for Distributed COPs—0
DC-DistADMM: ADMM Algorithm for Constrained Distributed Optimization over Directed Graphs—0
Decentralized Feature-Distributed Optimization for Generalized Linear Models—0
Decentralized Federated Learning via MIMO Over-the-Air Computation: Consensus Analysis and Performance Optimization—0
Decentralized gradient methods: does topology matter?—0
Decentralized Optimization on Compact Submanifolds by Quantized Riemannian Gradient Tracking—0
Decentralized Optimization with Distributed Features and Non-Smooth Objective Functions—0
Decentralized Personalized Federated Learning for Min-Max Problems—0
Correlated Quantization for Faster Nonconvex Distributed Optimization—0
Correlated quantization for distributed mean estimation and optimization—0
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