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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 181–190 of 536 papers

TitleStatusHype
Distributed Random Reshuffling Methods with Improved Convergence—0
Robust Optimization, Structure/Control co-design, Distributed Optimization, Monolithic Optimization, Robust Control, Parametric Uncertainty—0
Towards Scalable Multi-View Reconstruction of Geometry and Materials—0
Unbiased Compression Saves Communication in Distributed Optimization: When and How Much?—0
An Equivalent Circuit Approach to Distributed Optimization—0
Error Feedback Shines when Features are RareCode0
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
DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime—0
On the Finite-Time Behavior of Suboptimal Linear Model Predictive Control—0
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