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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 431–440 of 536 papers

TitleStatusHype
Tie-Line Characteristics based Partitioning for Distributed Optimization of Power Systems—0
Tighter Performance Theory of FedExProx—0
Toward Communication Efficient Adaptive Gradient Method—0
Towards privacy-preserving cooperative control via encrypted distributed optimization—0
Towards Scalable Multi-View Reconstruction of Geometry and Materials—0
Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning—0
Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent—0
Training Deep Neural Networks via Optimization Over Graphs—0
Trajectory Normalized Gradients for Distributed Optimization—0
Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs—0
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