Tighter underestimator for bivariate global optimization

IF 0.7 Q2 MATHEMATICS Afrika Matematika Pub Date : 2025-01-13 DOI:10.1007/s13370-024-01235-z
Mohand Ouanes
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引用次数: 0

Abstract

We propose in this paper a tighter underestimator for \(C^{2}\)-nonconvex bivariate functions. We show that it is tighter than the classical \(\alpha -\)BB underestimator. A branch and bound algorithm with this tighter underestimator is developed to solve bivariate global optimzation problems. The triangulation is used as an exhaustive subdivision, and a convex/concave test is added to accelerate the convergence of our branch and bound algorithm.

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二元全局优化的更紧低估器
本文提出了\(C^{2}\) -非凸二元函数的一个更严格的低估量。我们证明它比经典的\(\alpha -\) BB低估器更紧。针对二元全局优化问题,提出了一种具有这种严格低估量的分支定界算法。将三角剖分作为穷举细分,并加入凸/凹检验,加快了分支定界算法的收敛速度。
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来源期刊
Afrika Matematika
Afrika Matematika MATHEMATICS-
CiteScore
2.00
自引率
9.10%
发文量
96
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