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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
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
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
Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical AnalysisCode1
Distributed Training of Graph Convolutional Networks—0
Distributed optimization for nonrigid nano-tomographyCode0
A Unified Linear Speedup Analysis of Federated Averaging and Nesterov FedAvg—0
Federated Learning with Compression: Unified Analysis and Sharp Guarantees—0
Distributed Model Predictive Control with Reconfigurable Terminal Ingredients for Reference Tracking—0
Linear Convergent Decentralized Optimization with Compression—0
Advances in Asynchronous Parallel and Distributed Optimization—0
Distributed Collision-Free Motion Coordination on a Sphere: A Conic Control Barrier Function Approach—0
DEED: A General Quantization Scheme for Communication Efficiency in Bits—0
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