Treffer: Monotonous random search on a torus : Integral upper bounds for the complexity
Title:
Monotonous random search on a torus : Integral upper bounds for the complexity
Authors:
Source:
5th St. Petersburg workshop on simulation, St. Petersburg State University, St. Petersburg, Russia, 26 June-2 July 2005. Part IIJournal of statistical planning and inference. 137(12):4031-4047
Publisher Information:
Amsterdam; Lausanne; New York,NY: Elsevier Science, 2007.
Publication Year:
2007
Physical Description:
print, 1/4 p
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Computer science, Informatique, Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Analyse mathématique, Mathematical analysis, Calcul des variations et contrôle optimal, Calculus of variations and optimal control, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, Généralités, General topics, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Accélération de convergence, Acceleration of convergence, Méthodes numériques en programmation mathématique, optimisation et calcul variationnel, Numerical methods in mathematical programming, optimization and calculus of variations, Accélération convergence, Convergence acceleration, Aceleración convergencia, Analyse numérique, Numerical analysis, Análisis numérico, Borne supérieure, Upper bound, Cota superior, Convergence, Convergencia, Décision statistique, Statistical decision, Decisión estadística, Intégrale, Integral, Méthode optimisation, Optimization method, Método optimización, Méthode statistique, Statistical method, Método estadístico, Optimisation, Optimization, Optimización, Programmation mathématique, Mathematical programming, Programación matemática, Recherche aléatoire, Random search, Investigación aleatoria, Taux convergence, Convergence rate, Relación convergencia, 49XX, 65B99, 65Kxx, 90C30; 60J05; 65C05, Global optimization; Random search methods; Markov sequences
Document Type:
Konferenz
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Novgorod State University, Bol'shaya Sankt-Peterburgskaya 41, Veliky Novgorod 173003, Russian Federation
Mathematics Department, St. Petersburg University, Universitetsky av. 28, Petrodvorets, St. Petersburg 198504, Russian Federation
Mathematics Department, St. Petersburg University, Universitetsky av. 28, Petrodvorets, St. Petersburg 198504, Russian Federation
ISSN:
0378-3758
Rights:
Copyright 2007 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
Accession Number:
edscal.19061108
Database:
PASCAL Archive
Weitere Informationen
The paper consists of three parts. The first part is dedicated to a Markov monotonous random search on a general optimization space. Under certain restrictions, an upper bound for the complexity of search is presented in an integral form, suitable for further analysis. This estimate is applied to the case of a torus, where several specific results on the rate of convergence are obtained with the help of a supplementary optimization problem, discussed in Appendix.