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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 361–370 of 536 papers

TitleStatusHype
Distributed Resource Allocation Algorithms for Multi-Operator Cognitive Communication Systems—0
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural NetworksCode0
Supervised MPC control of large-scale electricity networks via clustering methods—0
Privacy-Preserving Distributed Optimization via Subspace Perturbation: A General FrameworkCode1
StochaLM: a Stochastic alternate Linearization Method for distributed optimization—0
Uncertain Multi-Agent Systems with Distributed Constrained Optimization Missions and Event-Triggered Communications: Application to Resource Allocation—0
DC-DistADMM: ADMM Algorithm for Constrained Distributed Optimization over Directed Graphs—0
Distributed and time-varying primal-dual dynamics via contraction analysis—0
Iterative Pre-Conditioning to Expedite the Gradient-Descent Method—0
Communication-efficient Variance-reduced Stochastic Gradient Descent—0
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