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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 191–200 of 536 papers

TitleStatusHype
Distributed Inexact Damped Newton Method: Data Partitioning and Load-Balancing—0
Cell Zooming with Masked Data for Off-Grid Small Cell Networks: Distributed Optimization Approach—0
Distributed Learning of Generalized Linear Causal Networks—0
Distributed Learning of Neural Lyapunov Functions for Large-Scale Networked Dissipative Systems—0
Distributed learning with compressed gradients—0
Distributed Linear Regression with Compositional Covariates—0
Anytime MiniBatch: Exploiting Stragglers in Online Distributed Optimization—0
Distributed Maximum Consensus over Noisy Links—0
New Bounds For Distributed Mean Estimation and Variance Reduction—0
Correlated quantization for distributed mean estimation and optimization—0
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