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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 131–140 of 536 papers

TitleStatusHype
TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data—0
FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation RetrievalCode0
Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity—0
Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of MultipliersCode0
Survey of Distributed Algorithms for Resource Allocation over Multi-Agent Systems—0
Optimal Data Splitting in Distributed Optimization for Machine Learning—0
Correlated Quantization for Faster Nonconvex Distributed Optimization—0
A Proximal Gradient Method With Probabilistic Multi-Gossip Communications for Decentralized Composite Optimization—0
Convergence Visualizer of Decentralized Federated Distillation with Reduced Communication Costs—0
Asynchronous Distributed Optimization with Delay-free Parameters—0
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