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Convergencia y optimización global en programación no lineal: Teoría y algoritmos

María Laura Taverna Antonio Aguirre Pío Hugo Aimar José Espinosa Aldo Vechietti

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Resumen/Descripción – provisto por el repositorio digital
A variety of decision-making problems that arise in areas as diverse as science, engineering, economics, among others, can be modeled mathematically as a constrained optimization problems. When we want to optimize and model real situations arise mathematical models using functions of different types, linear and nonlinear. Under conditions of convexity for functions there is a robust theory that guarantees the global solution of the problems. Most of engineering and applied science problems of interest involve optimization models with non-convex functions, which are complex to solve due to the existence of multiple local solutions. Convex optimization techniques applied to these non-convex problems do not guarantee the global optimum of the problem, so it is a matter of study, known as global optimization. In this thesis, the general characteristics of the deterministic algorithm Branch and bound (Branch and Bound) developed for non-convex models, studying their problems in some of the techniques proposed in the area of global optimization. We carried out a reformulation of the method "Improve and Branch" developed by Marcovecchio, Bergamini and Aguirre, deterministic algorithm and optimize branch type to be deployed to reduce levels and restrictions to reduce the feasible region.
Palabras clave – provistas por el repositorio digital

Optimization; Global; Nonconvex; Branch; Bound; Optimización; No convexo; Ramificar; Acotar

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No requiere 2007 Biblioteca Virtual de la Universidad Nacional del Litoral (SNRD) acceso abierto

Información

Tipo de recurso:

tesis

Idiomas de la publicación

  • español castellano

País de edición

Argentina

Fecha de publicación