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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 431–440 of 536 papers

TitleStatusHype
On Maintaining Linear Convergence of Distributed Learning and Optimization under Limited Communication—0
Sparsity Constrained Distributed Unmixing of Hyperspectral Data—0
Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free—0
Do Subsampled Newton Methods Work for High-Dimensional Data?—0
Predict Globally, Correct Locally: Parallel-in-Time Optimal Control of Neural Networks—0
Robust Learning from Untrusted SourcesCode0
99% of Distributed Optimization is a Waste of Time: The Issue and How to Fix it—0
Trajectory Normalized Gradients for Distributed Optimization—0
Distributed Nesterov gradient methods over arbitrary graphs—0
A continuous-time analysis of distributed stochastic gradient—0
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