KonstantinRiedl/CBOGlobalConvergenceAnalysis
Numerical illustration of a novel analysis framework for consensus-based optimization (CBO) and numerical experiments demonstrating the practicability of the method
This project offers a set of numerical experiments and illustrations for Consensus-Based Optimization (CBO) methods. It demonstrates how these multi-agent optimization techniques can find global minimums for complex, high-dimensional functions, even if they are non-convex or non-smooth. Researchers and practitioners in optimization, machine learning, and computational science can use this to understand the behavior and global convergence properties of CBO.
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Use this if you are a researcher or advanced practitioner investigating the theoretical and practical aspects of derivative-free global optimization, especially for challenging functions.
Not ideal if you are looking for a plug-and-play optimization library for routine engineering or business problems, as it focuses on research illustration rather than a production-ready tool.
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20
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4
Language
MATLAB
License
MIT
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Last pushed
Nov 13, 2023
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