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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
LocalNewton: Reducing Communication Bottleneck for Distributed Learning—0
Distributed Saddle-Point Problems: Lower Bounds, Near-Optimal and Robust Algorithms—0
Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time—0
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression—0
Logarithmically Quantized Distributed Optimization over Dynamic Multi-Agent Networks—0
Log-Scale Quantization in Distributed First-Order Methods: Gradient-based Learning from Distributed Data—0
Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression—0
Machine Learning for Large-Scale Optimization in 6G Wireless Networks—0
Machine Learning Infused Distributed Optimization for Coordinating Virtual Power Plant Assets—0
Markov Chain Block Coordinate Descent—0
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