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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 41–50 of 536 papers

TitleStatusHype
A Semi-Distributed Interior Point Algorithm for Optimal Coordination of Automated Vehicles at Intersections—0
Acceleration for Compressed Gradient Descent in Distributed Optimization—0
A Distributed ADMM-based Deep Learning Approach for Thermal Control in Multi-Zone Buildings under Demand Response Events—0
Accelerated Methods with Compressed Communications for Distributed Optimization Problems under Data Similarity—0
A Sequential Approximation Framework for Coded Distributed Optimization—0
A Stochastic Large-scale Machine Learning Algorithm for Distributed Features and Observations—0
A Novel Decentralized Algorithm for Coordinating the Optimal Power and Traffic Flows with EVs based on Variable Inner Loop Selection—0
Anytime MiniBatch: Exploiting Stragglers in Online Distributed Optimization—0
A Plug and Play Distributed Secondary Controller for Microgrids with Grid-Forming Inverters—0
An Online Optimization Approach for Multi-Agent Tracking of Dynamic Parameters in the Presence of Adversarial Noise—0
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