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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 481–490 of 536 papers

TitleStatusHype
Debiased distributed learning for sparse partial linear models in high dimensions—0
Stochastic, Distributed and Federated Optimization for Machine Learning—0
Adaptive Consensus ADMM for Distributed Optimization—0
Distributed Algorithms for Feature Extraction Off-loading in Multi-Camera Visual Sensor Networks—0
Accelerated Distributed Dual Averaging over Evolving Networks of Growing Connectivity—0
DOPE: Distributed Optimization for Pairwise Energies—0
Private Learning on Networks: Part II—0
Optimal algorithms for smooth and strongly convex distributed optimization in networksCode0
An Online Optimization Approach for Multi-Agent Tracking of Dynamic Parameters in the Presence of Adversarial Noise—0
Hemingway: Modeling Distributed Optimization Algorithms—0
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