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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 231–240 of 536 papers

TitleStatusHype
Coordinating Flexible Ramping Products with Dynamics of the Natural Gas Network—0
Convergence Theory of Generalized Distributed Subgradient Method with Random Quantization—0
Online Computation of Terminal Ingredients in Distributed Model Predictive Control for Reference Tracking—0
Distributed Learning of Neural Lyapunov Functions for Large-Scale Networked Dissipative Systems—0
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated LearningCode0
Can Competition Outperform Collaboration? The Role of Misbehaving Agents—0
Simultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots—0
On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network—0
Distributed Adversarial Training to Robustify Deep Neural Networks at ScaleCode0
Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression—0
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