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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 481–490 of 536 papers

TitleStatusHype
A KL-based Analysis Framework with Applications to Non-Descent Optimization Methods—0
ALADIN-α -- An open-source MATLAB toolbox for distributed non-convex optimization—0
ALADIN-β: A Distributed Optimization Algorithm for Solving MPCC Problems—0
Algorithm Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks—0
A Mirror Descent-Based Algorithm for Corruption-Tolerant Distributed Gradient Descent—0
An Equivalent Circuit Approach to Distributed Optimization—0
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing—0
An Exact Quantized Decentralized Gradient Descent Algorithm—0
An Integrated Optimization + Learning Approach to Optimal Dynamic Pricing for the Retailer with Multi-type Customers in Smart Grids—0
An Online Optimization Approach for Multi-Agent Tracking of Dynamic Parameters in the Presence of Adversarial Noise—0
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