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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 326–350 of 536 papers

TitleStatusHype
Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers—0
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning—0
Concentration of Non-Isotropic Random Tensors with Applications to Learning and Empirical Risk Minimization—0
Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications—0
Cost-efficient SVRG with Arbitrary Sampling—0
Design of heterogeneous multi-agent system for distributed computation—0
Convergent Adaptive Gradient Methods in Decentralized Optimization—0
Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning—0
Byzantine-Resilient Non-Convex Stochastic Gradient Descent—0
Wyner-Ziv Estimators for Distributed Mean Estimation with Side Information and OptimizationCode0
Linear Convergence of Distributed Mirror Descent with Integral Feedback for Strongly Convex Problems—0
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
Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies—0
Byzantine-Robust Learning on Heterogeneous Datasets via Resampling—0
Asynchronous Distributed Optimization with Stochastic Delays—0
Distributed Mirror Descent with Integral Feedback: Asymptotic Convergence Analysis of Continuous-time Dynamics—0
Graph neural networks-based Scheduler for Production planning problems using Reinforcement Learning—0
Distributed Optimization, Averaging via ADMM, and Network TopologyCode0
On Communication Compression for Distributed Optimization on Heterogeneous Data—0
GTAdam: Gradient Tracking with Adaptive Momentum for Distributed Online Optimization—0
Projected Push-Sum Gradient Descent-Ascent for Convex Optimizationwith Application to Economic Dispatch Problems—0
Distributed Personalized Gradient Tracking with Convex Parametric Models—0
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