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One dimensional consensus based algorithm for non-convex optimization

Authors
Young-Pil ChoiDOWAN KOO
Issue Date
Feb-2022
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Keywords
Consensus based optimization; Gradient-free optimization method; Non-convex optimization; Quantified optimization
Citation
APPLIED MATHEMATICS LETTERS, v.124, pp 107658-1 - 107658-8
Journal Title
APPLIED MATHEMATICS LETTERS
Volume
124
Start Page
107658-1
End Page
107658-8
URI
https://yscholarhub.yonsei.ac.kr/handle/2021.sw.yonsei/6336
DOI
10.1016/j.aml.2021.107658
ISSN
0893-9659
Abstract
We analyze the consensus based optimization method proposed in Pinnau et al. (2017) in one dimension. We rigorously provide a quantitative error estimate between the consensus point and global minimizer of a given objective function. Our analysis covers general objective functions; we do not require any structural assumption on the objective function.
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