A Derivative-free Two Level Random Search Method for Unconstrained Optimization

Idioma: inglés

Editorial: Springer, Palgrave Macmillan Apr 2021, 2021

3030685160 / 9783030685164

Serie: Libro 43 de 43 - SpringerBriefs in Optimization

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This item is printed on demand - Print on Demand Titel. Neuware -The book is intended for graduate students and researchers in mathematics, computer science, and operational research. The book presents a new derivative-free optimization method/algorithm based on randomly generated trial points in specified domains and where the best ones are selected at each iteration by using a number of rules. This method is different from many other well established methods presented in the literature and proves to be competitive for solving many unconstrained optimization problems with different structures and complexities, with a relative large number of variables. Intensive numerical experiments with 140 unconstrained optimization problems, with up to 500 variables, have shown that this approach is efficient and robust.Structured into 4 chapters, Chapter 1 is introductory. Chapter 2 is dedicated to presenting a two level derivative-free random search method for unconstrained optimization. It is assumed that the minimizing function is continuous, lower bounded and its minimum value is known. Chapter 3 proves the convergence of the algorithm. In Chapter 4, the numerical performances of the algorithm are shown for solving 140 unconstrained optimization problems, out of which 16 are real applications. This shows that the optimization process has two phases: the reduction phase and the stalling one. Finally, the performances of the algorithm for solving a number of 30 large-scale unconstrained optimization problems up to 500 variables are presented. These numerical results show that this approach based on the two level random search method for unconstrained optimization is able to solve a large diversity of problems with different structures and complexities.There are a number of open problems which refer to the following aspects: the selection of the number of trial or the number of the local trial points, the selection of the bounds of the domains where the trial points and the local trial points are randomly generated and a criterion for initiating the line search.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 132 pp. Englisch.…

N° de ref. del artículo 9783030685164

Título
A Derivative-free Two Level Random Search Method for Unconstrained Optimization
Autor
Neculai Andrei
Editorial
Springer, Palgrave Macmillan Apr 2021
Año de publicación
2021
Estado
Neu
Encuadernación
Taschenbuch
Idioma
inglés
ISBN 10
3030685160
ISBN 13
9783030685164
Peso del artículo
213 gramos
Dimensiones
235x155x8 mm
Serie
Libro 43 de 43: SpringerBriefs in Optimization

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