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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
Distributed optimization for nonrigid nano-tomographyCode0
A primal-dual perspective for distributed TD-learningCode0
Adding vs. Averaging in Distributed Primal-Dual OptimizationCode0
DASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity, and No Client SynchronizationCode0
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
Distributed Optimization using Heterogeneous Compute SystemsCode0
Communication Efficient Distributed Optimization using an Approximate Newton-type MethodCode0
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural NetworksCode0
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