Approximating the objective function's gradient using perceptrons for constrained minimization with application in drag reduction

DOI10.1016/j.cor.2015.05.012zbMath1349.90844OpenAlexW896926444MaRDI QIDQ342078

Mohammad Hasan, M. Dambrine

Publication date: 17 November 2016

Published in: Computers \& Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cor.2015.05.012

zbMATH Keywords

neural networksconstrained optimizationturbulencedrag reductionshear stressreduced gradient

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26) Derivative-free methods and methods using generalized derivatives (90C56)

Related Items

Approximating the objective function's gradient using perceptrons for constrained minimization with application in drag reduction ⋮ Rotorcraft low-noise trajectories design: black-box optimization using surrogates

Uses Software

• CPLEX
• minpack
• Global Optimization Test
• MinFinder
• AS 136

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