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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 381–390 of 536 papers

TitleStatusHype
New Bounds For Distributed Mean Estimation and Variance Reduction—0
Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets—0
The Geometry of Sign Gradient Descent—0
Distributed Optimization over Block-Cyclic Data—0
Is Local SGD Better than Minibatch SGD?—0
Distributed Averaging Methods for Randomized Second Order Optimization—0
Differentially Quantized Gradient Methods—0
Estimating the Error of Randomized Newton Methods: A Bootstrap Approach—0
Acceleration for Compressed Gradient Descent in Distributed Optimization—0
Manifold Identification for Ultimately Communication-Efficient Distributed OptimizationCode0
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