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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 51–60 of 536 papers

TitleStatusHype
Differentially Private Distributed Estimation and LearningCode0
Efficient Randomized Subspace Embeddings for Distributed Optimization under a Communication BudgetCode0
Distributed Optimization using Heterogeneous Compute SystemsCode0
Adding vs. Averaging in Distributed Primal-Dual OptimizationCode0
PIM-Opt: Demystifying Distributed Optimization Algorithms on a Real-World Processing-In-Memory SystemCode0
An Accelerated Communication-Efficient Primal-Dual Optimization Framework for Structured Machine LearningCode0
Accelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUsCode0
L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual FrameworkCode0
Distributed Optimization with Arbitrary Local SolversCode0
Communication Efficient Distributed Optimization using an Approximate Newton-type MethodCode0
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