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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 361–370 of 536 papers

TitleStatusHype
Popt4jlib: A Parallel/Distributed Optimization Library for Java—0
SLSGD: Secure and Efficient Distributed On-device Machine Learning—0
Predict Globally, Correct Locally: Parallel-in-Time Optimal Control of Neural Networks—0
Prescribed-time Convergent Distributed Multiobjective Optimization with Dynamic Event-triggered Communication—0
Coordinated Day-ahead Dispatch of Multiple Power Distribution Grids hosting Stochastic Resources: An ADMM-based Framework—0
Privacy-Preserving Distributed Market Mechanism for Active Distribution Networks—0
Privacy-Preserving Distributed Optimization and Learning—0
Privacy-Preserving Peer-to-Peer Energy Trading via Hybrid Secure Computations—0
Privacy-Preserving Push-Pull Method for Decentralized Optimization via State Decomposition—0
Private Learning on Networks—0
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