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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 31–40 of 536 papers

TitleStatusHype
Federated Optimization in Heterogeneous NetworksCode1
BAGUA: Scaling up Distributed Learning with System RelaxationsCode1
Beyond spectral gap: The role of the topology in decentralized learningCode1
A Federated Distributionally Robust Support Vector Machine with Mixture of Wasserstein Balls Ambiguity Set for Distributed Fault Diagnosis—0
Advances in Asynchronous Parallel and Distributed Optimization—0
A Dual Approach for Optimal Algorithms in Distributed Optimization over Networks—0
ADMM for Downlink Beamforming in Cell-Free Massive MIMO Systems—0
Accelerated consensus via Min-Sum Splitting—0
A Distributed Second-Order Algorithm You Can Trust—0
Acceleration in Distributed Optimization under Similarity—0
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