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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 451–475 of 536 papers

TitleStatusHype
Privacy-Preserving Push-Pull Method for Decentralized Optimization via State Decomposition—0
Private Learning on Networks—0
Private Learning on Networks: Part II—0
Problem-dependent convergence bounds for randomized linear gradient compression—0
Projected Push-Sum Gradient Descent-Ascent for Convex Optimizationwith Application to Economic Dispatch Problems—0
Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization—0
Provably Doubly Accelerated Federated Learning: The First Theoretically Successful Combination of Local Training and Communication Compression—0
Proximal gradient flow and Douglas-Rachford splitting dynamics: global exponential stability via integral quadratic constraints—0
Q-SHED: Distributed Optimization at the Edge via Hessian Eigenvectors Quantization—0
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations—0
Quantization Avoids Saddle Points in Distributed Optimization—0
Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free—0
Achieving Linear Speedup with ProxSkip in Distributed Stochastic Optimization—0
Rate Analysis of Coupled Distributed Stochastic Approximation for Misspecified Optimization—0
Real-Time Distributed Model Predictive Control with Limited Communication Data Rates—0
Recurrent Averaging Inequalities in Multi-Agent Control and Social Dynamics Modeling—0
Reducing the Communication of Distributed Model Predictive Control: Autoencoders and Formation Control—0
Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning—0
Graph neural networks-based Scheduler for Production planning problems using Reinforcement Learning—0
Residual-Evasive Attacks on ADMM in Distributed Optimization—0
Review of Mathematical Optimization in Federated Learning—0
Revisiting EXTRA for Smooth Distributed Optimization—0
Robust Distributed Optimization With Randomly Corrupted Gradients—0
Robust Optimization, Structure/Control co-design, Distributed Optimization, Monolithic Optimization, Robust Control, Parametric Uncertainty—0
ROML: A Robust Feature Correspondence Approach for Matching Objects in A Set of Images—0
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