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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 411–420 of 536 papers

TitleStatusHype
Multi-frequency calibration for DOA estimation with distributed sensors—0
Multi-Message Shuffled Privacy in Federated Learning—0
Multi-objective Distributed Optimization for Zonal Distribution System with Multi-Microgrids—0
Multi-Timescale Gradient Sliding for Distributed Optimization—0
MUSIC: Accelerated Convergence for Distributed Optimization With Inexact and Exact Methods—0
Network-aware EV charging and discharging in unbalanced distribution grids: A distributed, robust approach against communication failures—0
Network-Aware Value Stacking of Community Battery via Asynchronous Distributed Optimization—0
Network-GIANT: Fully distributed Newton-type optimization via harmonic Hessian consensus—0
Newton Meets Marchenko-Pastur: Massively Parallel Second-Order Optimization with Hessian Sketching and Debiasing—0
On Communication Compression for Distributed Optimization on Heterogeneous Data—0
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