Tighter convex underestimator for general twice differentiable function for global optimization
This paper proposes a new convex underestimator for general C 2 nonconvex functions. The new underestimator can be used in the branch and bound algorithm α BB for solving global optimization problems. We show that the new underestimator is tighter than the classical underestimator in the α BB method.
Authors
- Mohand Ouanès (ORCID: https://orcid.org/0000-0003-3868-9141)
- Djamel Zerrouki
Institutions
- Mouloud Mammeri University of Tizi-Ouzou (DZ)
Publication Details
- Journal
- RAIRO. Operations research
- Published
- 2024-09-13
- DOI
- https://doi.org/10.1051/ro/2024176
- Citations
- 1
- Primary Topic
- Advanced Optimization Algorithms Research
- Type
- article
- Field-Weighted Citation Impact
- 0.73