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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
Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical AnalysisCode1
Distributed Training of Graph Convolutional Networks—0
Distributed optimization for nonrigid nano-tomographyCode0
A Unified Linear Speedup Analysis of Federated Averaging and Nesterov FedAvg—0
Federated Learning with Compression: Unified Analysis and Sharp Guarantees—0
Distributed Model Predictive Control with Reconfigurable Terminal Ingredients for Reference Tracking—0
Linear Convergent Decentralized Optimization with Compression—0
Advances in Asynchronous Parallel and Distributed Optimization—0
Distributed Collision-Free Motion Coordination on a Sphere: A Conic Control Barrier Function Approach—0
DEED: A General Quantization Scheme for Communication Efficiency in Bits—0
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