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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 371–380 of 536 papers

TitleStatusHype
Deep Reinforcement Learning for QoS-Constrained Resource Allocation in Multiservice Networks—0
Decentralized gradient methods: does topology matter?—0
Improving Rate of Convergence via Gain Adaptation in Multi-Agent Distributed ADMM Framework—0
Multi-frequency calibration for DOA estimation with distributed sensors—0
Revisiting EXTRA for Smooth Distributed Optimization—0
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
Is Local SGD Better than Minibatch SGD?—0
Distributed Optimization over Block-Cyclic Data—0
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