Tighter convex underestimator for general twice differentiable function for global optimization

被引：0

作者：

Zerrouki, Djamel ^{[1
]}

Ouanes, Mohand ^{[1
]}

机构：

[1] Univ Mouloud Mammeri Tizi Ouzou, Fac Sci, LAROMAD, Tizi Ouzou 15000, Algeria

来源：

RAIRO-OPERATIONS RESEARCH | 2024年 / 58卷 / 06期

关键词：

Convex underestimator; global optimization; <italic>alpha</italic>BB method; ALPHA-BB; DCA;

D O I：

10.1051/ro/2024176

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

This paper proposes a new convex underestimator for general C2 nonconvex functions. The new underestimator can be used in the branch and bound algorithm alpha BB for solving global optimization problems. We show that the new underestimator is tighter than the classical underestimator in the alpha BB method.

引用

页码：5051 / 5062

页数：12

共 15 条

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