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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 241–250 of 536 papers

TitleStatusHype
A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting—0
Optimal Gradient Sliding and its Application to Distributed Optimization Under Similarity—0
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
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
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