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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 461–470 of 536 papers

TitleStatusHype
GoSGD: Distributed Optimization for Deep Learning with Gossip Exchange—0
Fundamental Resource Trade-offs for Encoded Distributed Optimization—0
A Stochastic Large-scale Machine Learning Algorithm for Distributed Features and Observations—0
SUCAG: Stochastic Unbiased Curvature-aided Gradient Method for Distributed Optimization—0
Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning—0
A Distributed Quasi-Newton Algorithm for Empirical Risk Minimization with Nonsmooth RegularizationCode0
Convergence rate of sign stochastic gradient descent for non-convex functions—0
ZOOpt: Toolbox for Derivative-Free OptimizationCode0
Optimal Algorithms for Distributed Optimization—0
Accelerated consensus via Min-Sum Splitting—0
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