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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 391–400 of 536 papers

TitleStatusHype
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
Scalable Centralized Deep Multi-Agent Reinforcement Learning via Policy Gradients—0
Seamless Integration: Sampling Strategies in Federated Learning Systems—0
Secure Architectures Implementing Trusted Coalitions for Blockchained Distributed Learning (TCLearn)—0
Short vs. Long-term Coordination of Drones: When Distributed Optimization Meets Deep Reinforcement Learning—0
Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under (L_0, L_1)-Smoothness—0
Simulation-Integrated Distributed Optimal Power Flow for Unbalanced Power Distribution Systems—0
Simultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots—0
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