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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 501–510 of 536 papers

TitleStatusHype
A Survey of Optimization Methods for Training DL Models: Theoretical Perspective on Convergence and Generalization—0
A Survey of Resilient Coordination for Cyber-Physical Systems Against Malicious Attacks—0
A Survey on Distributed Evolutionary Computation—0
A survey on secure decentralized optimization and learning—0
Asymptotic Network Independence in Distributed Stochastic Optimization for Machine Learning—0
Asynchronous Adaptation and Learning over Networks --- Part I: Modeling and Stability Analysis—0
Asynchronous Adaptation and Learning over Networks - Part II: Performance Analysis—0
Asynchronous Distributed ADMM for Large-Scale Optimization- Part I: Algorithm and Convergence Analysis—0
Asynchronous Distributed Optimization with Stochastic Delays—0
Asynchronous Distributed Optimization with Delay-free Parameters—0
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