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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 31–40 of 536 papers

TitleStatusHype
An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed OptimizationCode1
Beyond spectral gap (extended): The role of the topology in decentralized learningCode1
BAGUA: Scaling up Distributed Learning with System RelaxationsCode1
Distributed Optimization using Heterogeneous Compute SystemsCode0
Distributed Optimization with Arbitrary Local SolversCode0
Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over NetworksCode0
Distributed optimization for nonrigid nano-tomographyCode0
Efficient Randomized Subspace Embeddings for Distributed Optimization under a Communication BudgetCode0
A Distributed Quasi-Newton Algorithm for Primal and Dual Regularized Empirical Risk MinimizationCode0
Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of MultipliersCode0
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