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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
Federated TD Learning over Finite-Rate Erasure Channels: Linear Speedup under Markovian Sampling—0
Network-GIANT: Fully distributed Newton-type optimization via harmonic Hessian consensus—0
Distributed Coordination of Multi-Microgrids in Active Distribution Networks for Provisioning Ancillary Services—0
A Novel Decentralized Algorithm for Coordinating the Optimal Power and Traffic Flows with EVs based on Variable Inner Loop Selection—0
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis—0
D-SVM over Networked Systems with Non-Ideal Linking Conditions—0
A Survey on Distributed Evolutionary Computation—0
CEC: Crowdsourcing-based Evolutionary Computation for Distributed Optimization—0
Distributed Optimization for Quadratic Cost Functions over Large-Scale Networks with Quantized Communication and Finite-Time Convergence—0
On Degeneracy Issues in Multi-parametric Programming and Critical Region Exploration based Distributed Optimization in Smart Grid Operations—0
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