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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 381–390 of 536 papers

TitleStatusHype
Layer-wise Adaptive Gradient Sparsification for Distributed Deep Learning with Convergence Guarantees—0
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models: Extension—0
Learning-Accelerated ADMM for Distributed Optimal Power Flow—0
Learning Autonomy in Management of Wireless Random Networks—0
Distributed Model Predictive Control Design for Multi-agent Systems via Bayesian Optimization—0
Learning (With) Distributed Optimization—0
Leveraging Function Space Aggregation for Federated Learning at Scale—0
Limited Communications Distributed Optimization via Deep Unfolded Distributed ADMM—0
Linear Convergence of Distributed Mirror Descent with Integral Feedback for Strongly Convex Problems—0
On Linear Convergence of PI Consensus Algorithm under the Restricted Secant Inequality—0
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