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Found 3 papers, 2 papers with code
CoCoA: A General Framework for Communication-Efficient Distributed Optimization
The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning.
Primal-Dual Rates and Certificates
We propose an algorithm-independent framework to equip existing optimization methods with primal-dual certificates.
L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework
Despite the importance of sparsity in many large-scale applications, there are few methods for distributed optimization of sparsity-inducing objectives.
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