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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 491–500 of 536 papers

TitleStatusHype
Training Deep Neural Networks via Optimization Over Graphs—0
An Integrated Optimization + Learning Approach to Optimal Dynamic Pricing for the Retailer with Multi-type Customers in Smart Grids—0
Private Learning on Networks—0
Distributed Multi-Task Relationship Learning—0
A primal-dual method for conic constrained distributed optimization problems—0
Without-Replacement Sampling for Stochastic Gradient Methods—0
CoCoA: A General Framework for Communication-Efficient Distributed OptimizationCode0
Optimization for Large-Scale Machine Learning with Distributed Features and ObservationsCode0
Federated Optimization: Distributed Machine Learning for On-Device Intelligence—0
Geometrically Convergent Distributed Optimization with Uncoordinated Step-Sizes—0
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