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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 491–500 of 536 papers

TitleStatusHype
Distributed Optimization with Arbitrary Local SolversCode0
Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of MultipliersCode0
Efficient Randomized Subspace Embeddings for Distributed Optimization under a Communication BudgetCode0
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural NetworksCode0
Distributed Adversarial Training to Robustify Deep Neural Networks at ScaleCode0
Adding vs. Averaging in Distributed Primal-Dual OptimizationCode0
ZOOpt: Toolbox for Derivative-Free OptimizationCode0
Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over NetworksCode0
Shuffle-QUDIO: accelerate distributed VQE with trainability enhancement and measurement reductionCode0
Differentially Private Distributed Estimation and LearningCode0
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