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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 161–170 of 536 papers

TitleStatusHype
An Equivalent Circuit Approach to Distributed Optimization—0
Coordinated Frequency-Constrained Stochastic Economic Dispatch for Integrated Transmission and Distribution System via Distributed Optimization—0
Q-SHED: Distributed Optimization at the Edge via Hessian Eigenvectors Quantization—0
On the Finite-Time Behavior of Suboptimal Linear Model Predictive Control—0
DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime—0
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
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