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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 171–180 of 536 papers

TitleStatusHype
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
Learning (With) Distributed Optimization—0
Continuous-Time Distributed Dynamic Programming for Networked Multi-Agent Markov Decision Processes—0
Federated K-Means Clustering via Dual Decomposition-based Distributed Optimization—0
Differentially Private Distributed Estimation and LearningCode0
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