| Issue |
RAIRO-Oper. Res.
Volume 58, Number 6, November-December 2024
|
|
|---|---|---|
| Page(s) | 5051 - 5062 | |
| DOI | https://doi.org/10.1051/ro/2024176 | |
| Published online | 06 December 2024 | |
Tighter convex underestimator for general twice differentiable function for global optimization
LAROMAD, Faculté des Sciences, Université Mouloud Mammeri de Tizi Ouzou, 15000 Tizi-Ouzou, Algeria
* Corresponding author: mohand.ouanes@ummto.dz
Received:
1
June
2022
Accepted:
11
September
2024
This paper proposes a new convex underestimator for general C2 nonconvex functions. The new underestimator can be used in the branch and bound algorithm αBB for solving global optimization problems. We show that the new underestimator is tighter than the classical underestimator in the αBB method.
Mathematics Subject Classification: 65K05 / 90C30 / 90C34
Key words: Convex underestimator / global optimization / αBB method
© The authors. Published by EDP Sciences, ROADEF, SMAI 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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