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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 61–70 of 536 papers

TitleStatusHype
A survey on secure decentralized optimization and learning—0
Asymptotic Network Independence in Distributed Stochastic Optimization for Machine Learning—0
An Integrated Optimization + Learning Approach to Optimal Dynamic Pricing for the Retailer with Multi-type Customers in Smart Grids—0
A Differential Private Method for Distributed Optimization in Directed Networks via State Decomposition—0
An Exact Quantized Decentralized Gradient Descent Algorithm—0
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing—0
Debiased distributed learning for sparse partial linear models in high dimensions—0
Accelerating variational quantum algorithms with multiple quantum processors—0
An Equivalent Circuit Approach to Distributed Optimization—0
Accelerated Distributed Optimization with Compression and Error Feedback—0
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