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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 141–150 of 536 papers

TitleStatusHype
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise—0
A primal-dual perspective for distributed TD-learningCode0
Privacy-Preserving Distributed Market Mechanism for Active Distribution Networks—0
On Linear Convergence of PI Consensus Algorithm under the Restricted Secant Inequality—0
CORE: Common Random Reconstruction for Distributed Optimization with Provable Low Communication Complexity—0
Limited Communications Distributed Optimization via Deep Unfolded Distributed ADMM—0
Linear Speedup of Incremental Aggregated Gradient Methods on Streaming Data—0
Distributed Optimization via Gradient Descent with Event-Triggered Zooming over Quantized Communication—0
Moreau Envelope ADMM for Decentralized Weakly Convex Optimization—0
Privacy-Preserving Push-Pull Method for Decentralized Optimization via State Decomposition—0
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