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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 311–320 of 536 papers

TitleStatusHype
Gradient-Tracking over Directed Graphs for solving Leaderless Multi-Cluster Games—0
Decentralized Riemannian Gradient Descent on the Stiefel ManifoldCode1
Distributed Second Order Methods with Fast Rates and Compressed Communication—0
Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization—0
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
Design of heterogeneous multi-agent system for distributed computation—0
Convergent Adaptive Gradient Methods in Decentralized Optimization—0
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