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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 461–470 of 536 papers

TitleStatusHype
Quantization Avoids Saddle Points in Distributed Optimization—0
Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free—0
Achieving Linear Speedup with ProxSkip in Distributed Stochastic Optimization—0
Rate Analysis of Coupled Distributed Stochastic Approximation for Misspecified Optimization—0
Real-Time Distributed Model Predictive Control with Limited Communication Data Rates—0
Recurrent Averaging Inequalities in Multi-Agent Control and Social Dynamics Modeling—0
Reducing the Communication of Distributed Model Predictive Control: Autoencoders and Formation Control—0
Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning—0
Graph neural networks-based Scheduler for Production planning problems using Reinforcement Learning—0
Residual-Evasive Attacks on ADMM in Distributed Optimization—0
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