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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 41–50 of 536 papers

TitleStatusHype
Distributed Optimization with Arbitrary Local SolversCode0
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed OptimizationCode0
Efficient Randomized Subspace Embeddings for Distributed Optimization under a Communication BudgetCode0
Distributed Adversarial Training to Robustify Deep Neural Networks at ScaleCode0
Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of MultipliersCode0
Differentially Private Distributed Estimation and LearningCode0
Distributed Optimization, Averaging via ADMM, and Network TopologyCode0
Adding vs. Averaging in Distributed Primal-Dual OptimizationCode0
Cooperative Tuning of Multi-Agent Optimal Control SystemsCode0
PIM-Opt: Demystifying Distributed Optimization Algorithms on a Real-World Processing-In-Memory SystemCode0
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