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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 411–420 of 536 papers

TitleStatusHype
Distributed Optimization by Network Flows with Spatio-Temporal Compression—0
Spatio-Temporal Communication Compression in Distributed Prime-Dual Flows—0
StochaLM: a Stochastic alternate Linearization Method for distributed optimization—0
On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network—0
Stochastic, Distributed and Federated Optimization for Machine Learning—0
Stochastic Distributed Optimization for Machine Learning from Decentralized Features—0
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis—0
Straggler Mitigation in Distributed Optimization Through Data Encoding—0
Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers—0
Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction—0
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