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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 271–280 of 536 papers

TitleStatusHype
KKT Conditions, First-Order and Second-Order Optimization, and Distributed Optimization: Tutorial and Survey—0
Distributed Privacy-Preserving Electric Vehicle Charging Control Based on Secret Sharing—0
Distributed Optimization using Heterogeneous Compute SystemsCode0
Unbiased Single-scale and Multi-scale Quantizers for Distributed OptimizationCode1
Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation AnalysisCode0
Distributed Online Optimization with Byzantine Adversarial Agents—0
Toward Communication Efficient Adaptive Gradient Method—0
Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees—0
On the Convergence of Decentralized Adaptive Gradient Methods—0
The Minimax Complexity of Distributed Optimization—0
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