Tighter convex underestimator for general twice differentiable function for global optimization

\nThis 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.\n

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Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2024-12-06
DOI
https://doi.org/10.1051/ro/2024176/pdf
Primary Topic
Education Methods and Technologies
Type
article
Field-Weighted Citation Impact
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article

Tighter convex underestimator for general twice differentiable function for global optimization

Springer Link (Chiba Institute of Technology)
Education Methods and Technologies
article

Tighter convex underestimator for general twice differentiable function for global optimization

article en

Abstract

\nThis 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.\n

Springer Link (Chiba Institute of Technology)
Mouloud Mammeri University of Tizi-Ouzou (DZ)
Openalex Percentile: Top 40%
Education Methods and Technologies
DOIOpen Access PDFOpenAlex
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Tighter convex underestimator for general twice differentiable function for global optimization · Springer Link (Chiba Institute of Technology) (2024) | TGRS Research Map | TGRS