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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 321–330 of 536 papers

TitleStatusHype
Cost-efficient SVRG with Arbitrary Sampling—0
Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning—0
Byzantine-Resilient Non-Convex Stochastic Gradient Descent—0
Linear Convergence of Distributed Mirror Descent with Integral Feedback for Strongly Convex Problems—0
Wyner-Ziv Estimators for Distributed Mean Estimation with Side Information and OptimizationCode0
Sparse sketches with small inversion bias—0
Distributed Stochastic Consensus Optimization with Momentum for Nonconvex Nonsmooth Problems—0
Widely-distributed Radar Imaging Based on Consensus ADMM—0
Distributed Saddle-Point Problems: Lower Bounds, Near-Optimal and Robust Algorithms—0
Distributed Resource Allocation with Multi-Agent Deep Reinforcement Learning for 5G-V2V CommunicationCode1
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