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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 301–325 of 536 papers

TitleStatusHype
Distributed Very Large Scale Bundle Adjustment by Global Camera Consensus—0
DOPE: Distributed Optimization for Pairwise Energies—0
Do Subsampled Newton Methods Work for High-Dimensional Data?—0
Double Quantization for Communication-Efficient Distributed Optimization—0
DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization—0
D-SVM over Networked Systems with Non-Ideal Linking Conditions—0
Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling—0
DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime—0
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections—0
Dynamic Incentive Strategies for Smart EV Charging Stations: An LLM-Driven User Digital Twin Approach—0
EControl: Fast Distributed Optimization with Compression and Error Control—0
Efficient Distributed Optimization under Heavy-Tailed Noise—0
ELM-Based Distributed Cooperative Learning Over Networks—0
End-to-End Quality-of-Service Assurance with Autonomous Systems: 5G/6G Case Study—0
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization—0
Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning—0
Estimating the Error of Randomized Newton Methods: A Bootstrap Approach—0
Estimation Network Design framework for efficient distributed optimization—0
Assessing the Impacts of Nonideal Communications on Distributed Optimal Power Flow Algorithms—0
Exploring Scaling Laws for Local SGD in Large Language Model Training—0
Fast Adaptive Federated Bilevel Optimization—0
Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty—0
Fast Distributed Optimization over Directed Graphs under Malicious Attacks using Trust—0
FedADMM: A Federated Primal-Dual Algorithm Allowing Partial Participation—0
FedECADO: A Dynamical System Model of Federated Learning—0
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