A set of examples of global and discrete optimization. Applications of Bayesian heuristic approach
type: Item
instance of: scholarly article
title: A set of examples of global and discrete optimization. Applications of Bayesian heuristic approach
zbMATH Open document ID: 0963.90076
author: Jonas Mockus
published in: Applied Optimization
publication date: 2000-09-14T00:00:00Z
review text: The book presents Bayesian heuristic approach as a tool to improve heuristic solutions for several well known optimization problems. It is intended to be a tool to be used as material in a operations research course together with software available in the related Internet sites (\url{http://mockus.org/optimum} and others). After a short theoretical introduction, most of the book is dedicated to examples and software description. A Global Minnimizer is presented which includes Bayesian search, clustering, and Monte Carlo search, together with three local optimization methods (two clasical ones and one proposed by the author). Part III of the book presents examples of application of the software at several problems. Theoretical background in optimization methods and other subjects treated is required to read the book. Part of the material in the mentioned site is in Lithuanian.
reviewed by: Irene Loiseau
Mathematics Subject Classification ID: 90C59
Mathematics Subject Classification ID: 90-01
Mathematics Subject Classification ID: 90Cxx
Mathematics Subject Classification ID: 65K05
zbMATH DE Number: 1506310
zbMATH Keywords: global optimization
zbMATH Keywords: heuristics
zbMATH Keywords: randomization
MaRDI profile type: Publication
description: scientific article; zbMATH DE number 1506310