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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 131–140 of 536 papers

TitleStatusHype
A continuous-time analysis of distributed stochastic gradient—0
Accelerating Distributed Optimization: A Primal-Dual Perspective on Local Steps—0
Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees—0
A KL-based Analysis Framework with Applications to Non-Descent Optimization Methods—0
Cost-efficient SVRG with Arbitrary Sampling—0
Correlation Aware Sparsified Mean Estimation Using Random Projection—0
Asynchronous Forward Bounding for Distributed COPs—0
Correlated Quantization for Faster Nonconvex Distributed Optimization—0
CSWA: Aggregation-Free Spatial-Temporal Community Sensing—0
Correlated quantization for distributed mean estimation and optimization—0
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