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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 241–250 of 536 papers

TitleStatusHype
Distributed Random Reshuffling over Networks—0
Distributed Resource Allocation Algorithms for Multi-Operator Cognitive Communication Systems—0
Correlated quantization for distributed mean estimation and optimization—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
Asynchronous Distributed Optimization with Delay-free Parameters—0
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