Combination of two underestimators for univariate global optimization

DOI10.1051/ro/2018013zbMath1397.65090OpenAlexW2785314424MaRDI QIDQ4579891

Mohand Ouanes, Mohammed Chebbah, Ahmed Zidna

Publication date: 10 August 2018

Published in: RAIRO - Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1051/ro/2018013

zbMATH Keywords

global optimizationbranch and bound{\(\alpha\)}BB methodquadratic underestimator

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Semi-infinite programming (90C34)

Cites Work

• A review of recent advances in global optimization
• Tight convex underestimators for \({{\mathcal C}^2}\)-continuous problems. I: Univariate functions
• Tight convex underestimators for \({\mathcal{C}^2}\)-continuous problems. II: Multivariate functions
• A practical guide to splines
• New interval analysis support functions using gradient information in a global minimization algorithm
• \(\alpha BB\): A global optimization method for general constrained nonconvex problems
• An information global minimization algorithm using the local improvement technique
• New quadratic lower bound for multivariate functions in global optimization
• Optimal centers in branch-and-prune algorithms for univariate global optimization
• Convex quadratic underestimation and Branch and Bound for univariate global optimization with one nonconvex constraint
• Finite exact branch-and-bound algorithms for concave minimization over polytopes

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