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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 151–160 of 536 papers

TitleStatusHype
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
Distributed Random Reshuffling Methods with Improved Convergence—0
Just One Byte (per gradient): A Note on Low-Bandwidth Decentralized Language Model Finetuning Using Shared RandomnessCode1
Robust Optimization, Structure/Control co-design, Distributed Optimization, Monolithic Optimization, Robust Control, Parametric Uncertainty—0
Towards Scalable Multi-View Reconstruction of Geometry and Materials—0
Unbiased Compression Saves Communication in Distributed Optimization: When and How Much?—0
An Equivalent Circuit Approach to Distributed Optimization—0
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