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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 331–340 of 536 papers

TitleStatusHype
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
On Degeneracy Issues in Multi-parametric Programming and Critical Region Exploration based Distributed Optimization in Smart Grid Operations—0
On Distributed Adaptive Optimization with Gradient Compression—0
Online Computation of Terminal Ingredients in Distributed Model Predictive Control for Reference Tracking—0
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