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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 201–210 of 536 papers

TitleStatusHype
Distributed Mirror Descent with Integral Feedback: Asymptotic Convergence Analysis of Continuous-time Dynamics—0
Distributed Model Predictive Control with Reconfigurable Terminal Ingredients for Reference Tracking—0
Distributed model predictive control without terminal cost under inexact distributed optimization—0
Parallel Momentum Methods Under Biased Gradient Estimations—0
Deep Reinforcement Learning for QoS-Constrained Resource Allocation in Multiservice Networks—0
Distributed-MPC with Data-Driven Estimation of Bus Admittance Matrix in Voltage Control—0
BALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with Application to Distributed Optimization—0
Distributed Nesterov gradient methods over arbitrary graphs—0
A Mirror Descent-Based Algorithm for Corruption-Tolerant Distributed Gradient Descent—0
Deep Learning for Distributed Optimization: Applications to Wireless Resource Management—0
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