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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 291–300 of 536 papers

TitleStatusHype
Distributed Resource Allocation Algorithms for Multi-Operator Cognitive Communication Systems—0
Distributed saddle point problems for strongly concave-convex functions—0
Distributed Second Order Methods with Fast Rates and Compressed Communication—0
Distributed Sketching for Randomized Optimization: Exact Characterization, Concentration and Lower Bounds—0
Distributed Stochastic Consensus Optimization with Momentum for Nonconvex Nonsmooth Problems—0
Distributed Stochastic Variance Reduced Gradient Methods and A Lower Bound for Communication Complexity—0
Distributed System Identification for Linear Stochastic Systems with Binary Sensors—0
Distributed Traffic Signal Control via Coordinated Maximum Pressure-plus-Penalty—0
Distributed Unmixing of Hyperspectral Data With Sparsity Constraint—0
Distributed Utility Optimization in Vehicular Communication Systems—0
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