Random Search Procedures for Global Optimization
Solving optimization problems from engineering, as, e.g., parameter—or process—optimization problems $$\displaystyle \min F (x) \mbox{ s.t. } x \in D, $$ where D is a subset of \(\mathbb {R}^n\) , one meets often the following situation: (a) One should fi
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Kurt Marti
Optimization Under Stochastic Uncertainty Methods, Control and Random Search Methods
International Series in Operations Research & Management Science Volume 296
Series Editor Camille C. Price Department of Computer Science, Stephen F. Austin State University, Nacogdoches, TX, USA Associate Editor Joe Zhu Foisie Business School, Worcester Polytechnic Institute, Worcester, MA, USA Founding Editor Frederick S. Hillier Stanford University, Stanford, CA, USA
More information about this series at http://www.springer.com/series/6161
Kurt Marti
Optimization Under Stochastic Uncertainty Methods, Control and Random Search Methods
Kurt Marti Institute for Mathematics and Computer Science University of Bundeswehr Munich Munich, Germany
ISSN 0884-8289 ISSN 2214-7934 (electronic) International Series in Operations Research & Management Science ISBN 978-3-030-55661-7 ISBN 978-3-030-55662-4 (eBook) https://doi.org/10.1007/978-3-030-55662-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
Optimization problems in practice depend mostly on several model parameters, noise factors, uncontrollable parameters, etc., which are not given fixed quantities at the planning stage. Typical examples from engineering and economics/operations research are: material parameters, manufacturing errors, tolerances, noise terms, demand parameters, technological coefficients in input–output functions, cost factors, etc. Due to several types of stochastic uncertainties (physical uncertainty, economic uncertainty, statistical uncertainty, and model uncertainty), th
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