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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 226–250 of 536 papers

TitleStatusHype
Power Bundle Adjustment for Large-Scale 3D ReconstructionCode2
Optimization-Based Ramping Reserve Allocation of BESS for AGC Enhancement—0
Distributed Dynamic Safe Screening Algorithms for Sparse Regularization—0
FedADMM: A Federated Primal-Dual Algorithm Allowing Partial Participation—0
Competition-Based Resilience in Distributed Quadratic Optimization—0
Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point—0
Distributed Sketching for Randomized Optimization: Exact Characterization, Concentration and Lower Bounds—0
Distributed Dual Quaternion Based Localization of Visual Sensor Networks—0
Optimal Methods for Convex Risk Averse Distributed Optimization—0
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms—0
Correlated quantization for distributed mean estimation and optimization—0
Acceleration of Federated Learning with Alleviated Forgetting in Local TrainingCode1
Distributed Methods with Absolute Compression and Error Compensation—0
Distributed-MPC with Data-Driven Estimation of Bus Admittance Matrix in Voltage Control—0
Multi-objective Distributed Optimization for Zonal Distribution System with Multi-Microgrids—0
Signal Decomposition Using Masked Proximal OperatorsCode1
Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning—0
Distributed saddle point problems for strongly concave-convex functions—0
Spatial Reuse in Dense Wireless Areas: A Cross-layer Optimization Approach via ADMM—0
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing—0
Communication Efficient Federated Learning via Ordered ADMM in a Fully Decentralized Setting—0
DASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity, and No Client Synchronization—0
Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning—0
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?Code1
End-to-End Quality-of-Service Assurance with Autonomous Systems: 5G/6G Case Study—0
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