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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 51–60 of 536 papers

TitleStatusHype
A primal-dual method for conic constrained distributed optimization problems—0
Acceleration in Distributed Optimization under Similarity—0
A Provably Communication-Efficient Asynchronous Distributed Inference Method for Convex and Nonconvex Problems—0
A Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flow—0
Asynchronous Distributed Optimization with Stochastic Delays—0
A Sequential Approximation Framework for Coded Distributed Optimization—0
A Stochastic Large-scale Machine Learning Algorithm for Distributed Features and Observations—0
A Survey of Optimization Methods for Training DL Models: Theoretical Perspective on Convergence and Generalization—0
Asynchronous Distributed Optimization with Delay-free Parameters—0
An Online Optimization Approach for Multi-Agent Tracking of Dynamic Parameters in the Presence of Adversarial Noise—0
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