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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 51–75 of 536 papers

TitleStatusHype
ALADIN-β: A Distributed Optimization Algorithm for Solving MPCC Problems—0
Combining Graph Attention Networks and Distributed Optimization for Multi-Robot Mixed-Integer Convex Programming—0
Convergence Theory of Flexible ALADIN for Distributed Optimization—0
Collaborative Satisfaction of Long-Term Spatial Constraints in Multi-Agent Systems: A Distributed Optimization Approach (extended version)—0
Communication Efficient Federated Learning with Linear Convergence on Heterogeneous Data—0
Byzantine-Resilient Federated Learning via Distributed Optimization—0
Accelerated Distributed Optimization with Compression and Error Feedback—0
From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges—0
Distributed Pose Graph Optimization using the Splitting Method based on the Alternating Direction Method of Multipliers—0
Opportunistic Routing in Wireless Communications via Learnable State-Augmented PoliciesCode0
Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs—0
Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton—0
Smoothed Normalization for Efficient Distributed Private Optimization—0
Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under (L_0, L_1)-Smoothness—0
Efficient Distributed Optimization under Heavy-Tailed Noise—0
A Survey of Optimization Methods for Training DL Models: Theoretical Perspective on Convergence and Generalization—0
Communication-Efficient Distributed Kalman Filtering using ADMM—0
Distributed Model Predictive Control Design for Multi-agent Systems via Bayesian Optimization—0
Distributed Convex Optimization with State-Dependent (Social) Interactions over Random Networks—0
Accelerated Methods with Compressed Communications for Distributed Optimization Problems under Data Similarity—0
Towards privacy-preserving cooperative control via encrypted distributed optimization—0
Information-Geometric Barycenters for Bayesian Federated Learning—0
Deep Distributed Optimization for Large-Scale Quadratic Programming—0
Fractional Order Distributed Optimization—0
Review of Mathematical Optimization in Federated Learning—0
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