One dimensional consensus based algorithm for non-convex optimization

被引：1
作者：
Choi, Young-Pil [1 ]
Koo, Dowan [1 ]
机构：
[1] Yonsei Univ, Dept Math, Seoul 03722, South Korea
来源：
APPLIED MATHEMATICS LETTERS | 2022年 / 124卷
关键词：
Consensus based optimization; Gradient-free optimization method; Non-convex optimization; Quantified optimization; GLOBAL OPTIMIZATION;
D O I：
10.1016/j.aml.2021.107658
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
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. (C) 2021 Elsevier Ltd. All rights reserved.
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页数：8
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