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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 421–430 of 536 papers

TitleStatusHype
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
Online distributed optimization for spatio-temporally constrained real-time peer-to-peer energy trading—0
Online Distributed Optimization on Dynamic Networks—0
On Maintaining Linear Convergence of Distributed Learning and Optimization under Limited Communication—0
On the Convergence of Decentralized Adaptive Gradient Methods—0
On the Finite-Time Behavior of Suboptimal Linear Model Predictive Control—0
Optimal Algorithms for Distributed Optimization—0
Optimal Data Splitting in Distributed Optimization for Machine Learning—0
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