Result: Local tuning and partition strategies for diagonal GO methods
Title:
Local tuning and partition strategies for diagonal GO methods
Authors:
Source:
Numerische Mathematik. 94(1):93-106
Publisher Information:
Berlin; Heidelberg; New York, NY: Springer, 2003.
Publication Year:
2003
Physical Description:
print, 25 ref
Original Material:
INIST-CNRS
Subject Terms:
Mathematics, Mathématiques, Mechanics acoustics, Mécanique et acoustique, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Méthodes numériques en programmation mathématique, optimisation et calcul variationnel, Numerical methods in mathematical programming, optimization and calculus of variations, Programmation mathématique numérique, Numerical methods in mathematical programming, Sciences appliquees, Applied sciences, Recherche operationnelle. Gestion, Operational research. Management science, Recherche opérationnelle et modèles formalisés de gestion, Operational research and scientific management, Programmation mathématique, Mathematical programming, Convergence méthode numérique, Convergence of numerical methods, Etude comparative, Comparative study, Estudio comparativo, Fonction objectif, Objective function, Función objetivo, Modèle n dimensions, Multidimensional model, Modelo n dimensiones, Méthode géométrique, Geometrical method, Método geométrico, Méthode optimisation, Optimization method, Método optimización, Méthode partition, Partition method, Método partición, Optimum global, Global optimum, Optimo global, Programmation mathématique, Mathematical programming, Programación matemática, Algorithme diagonal, Diagonal algorithm, Problème Lipschitz, Lipschitz problem
Document Type:
Academic journal
Article
File Description:
text
Language:
English
Author Affiliations:
University of Nizhni Novgorod, Gagarin Av., 23, 603950 Nizhni Novgorod, Russian Federation
Istituto per la Sistemistica e l'Informatica - C.N.R., c/o D.E.I.S. - Università della, Calabria, 87036 Rende (CS), Italy
Istituto per la Sistemistica e l'Informatica - C.N.R., c/o D.E.I.S. - Università della, Calabria, 87036 Rende (CS), Italy
ISSN:
0029-599X
Rights:
Copyright 2003 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics
Operational research. Management
Operational research. Management
Accession Number:
edscal.14657117
Database:
PASCAL Archive
Further Information
In this paper, global optimization (GO) Lipschitz problems are considered where the multi-dimensional multiextremal objective function is determined over a hyperinterval. An efficient one-dimensional GO method using local tuning on the behavior of the objective function is generalized to the multi-dimensional case by the diagonal approach using two partition strategies. Global convergence conditions are established for the obtained diagonal geometric methods. Results of a wide numerical comparison show a strong acceleration reached by the new methods working with estimates of the local Lipschitz constants over different subregions of the search domain in comparison with the traditional approach.