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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 351–360 of 536 papers

TitleStatusHype
Approximate Gradient Coding with Optimal Decoding—0
Federated Accelerated Stochastic Gradient DescentCode1
Byzantine-Robust Learning on Heterogeneous Datasets via BucketingCode1
Distributed Newton Can Communicate Less and Resist Byzantine Workers—0
Distributed Optimization for Massive Connectivity—0
Anytime MiniBatch: Exploiting Stragglers in Online Distributed Optimization—0
ALADIN-α -- An open-source MATLAB toolbox for distributed non-convex optimization—0
MANGO: A Python Library for Parallel Hyperparameter TuningCode1
Recurrent Averaging Inequalities in Multi-Agent Control and Social Dynamics Modeling—0
FedSplit: An algorithmic framework for fast federated optimization—0
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