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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 231–240 of 536 papers

TitleStatusHype
Distributed Optimization via Gradient Descent with Event-Triggered Zooming over Quantized Communication—0
Distributed Optimization via Kernelized Multi-armed Bandits—0
Communication Efficient Federated Learning with Linear Convergence on Heterogeneous Data—0
Distributed Optimization with Efficient Communication, Event-Triggered Solution Enhancement, and Operation Stopping—0
Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement—0
Distributed Optimization with Gradient Tracking over Heterogeneous Delay-Prone Directed Networks—0
Distributed Personalized Gradient Tracking with Convex Parametric Models—0
Distributed Pose Graph Optimization using the Splitting Method based on the Alternating Direction Method of Multipliers—0
Asynchronous Distributed Optimization with Delay-free Parameters—0
Aiding Global Convergence in Federated Learning via Local Perturbation and Mutual Similarity Information—0
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