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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 441–450 of 536 papers

TitleStatusHype
Hyperspectral Unmixing Based on Clustered Multitask Networks—0
Stochastic Distributed Optimization for Machine Learning from Decentralized Features—0
Solving Non-smooth Constrained Programs with Lower Complexity than O(1/ ): A Primal-Dual Homotopy Smoothing Approach—0
Markov Chain Block Coordinate Descent—0
Distributed Convex Optimization With Limited Communications—0
Distributed optimization in wireless sensor networks: an island-model framework—0
Sparsified SGD with MemoryCode0
A Dual Approach for Optimal Algorithms in Distributed Optimization over Networks—0
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks—0
An Exact Quantized Decentralized Gradient Descent Algorithm—0
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