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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 221–230 of 536 papers

TitleStatusHype
Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums—0
Distributed Optimization in Distribution Systems with Grid-Forming and Grid-Supporting Inverters—0
On Distributed Adaptive Optimization with Gradient Compression—0
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed OptimizationCode0
Understanding A Class of Decentralized and Federated Optimization Algorithms: A Multi-Rate Feedback Control Perspective—0
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
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