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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 311–320 of 536 papers

TitleStatusHype
EControl: Fast Distributed Optimization with Compression and Error Control—0
Efficient Distributed Optimization under Heavy-Tailed Noise—0
ELM-Based Distributed Cooperative Learning Over Networks—0
End-to-End Quality-of-Service Assurance with Autonomous Systems: 5G/6G Case Study—0
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization—0
Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning—0
Estimating the Error of Randomized Newton Methods: A Bootstrap Approach—0
Estimation Network Design framework for efficient distributed optimization—0
Assessing the Impacts of Nonideal Communications on Distributed Optimal Power Flow Algorithms—0
Exploring Scaling Laws for Local SGD in Large Language Model Training—0
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