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Solving Optimization Problems with the Heuristic Kalman Algorithm

New Stochastic Methods

  • Book
  • © 2024

Overview

Authors:
  1. Rosario Toscano

    1. École Nationale d’Ingénieurs de Saint-Etienne, Saint-Etienne, France

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  • Provides a review of the main deterministic and stochastic optimization methods
  • Presents material that industrial engineers, postgraduates, and undergraduates in systems design will find useful
  • Large coverage of practical optimization problems

Part of the book series: Springer Optimization and Its Applications (SOIA, volume 212)

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About this book

This text focuses on simple and easy-to-use design strategies for solving complex engineering problems that arise in several fields of engineering design, namely non-convex optimization problems. 


The main optimization tool used in this book to tackle the problem of nonconvexity is the Heuristic Kalman Algorithm (HKA). The main characteristic of HKA is the use of a stochastic search mechanism to solve a given optimization problem. From a computational point of view, the use of a stochastic search procedure appears essential for dealing with non-convex problems.


The topics discussed in this monograph include basic definitions and concepts from the classical optimization theory, the notion of the acceptable solution, machine learning, the concept of preventive maintenance, and more. 


The Heuristic Kalman Algorithm discussed in this book applies to many fields such as robust structured control, electrical engineering, mechanical engineering, machine learning, reliability, and preference models. This large coverage of practical optimization problems makes this text very useful to those working on and researching systems design. The intended audience includes industrial engineers, postgraduates, and final-year undergraduates in various fields of systems design. 


Keywords

Table of contents (8 chapters)

  1. Front Matter

    Pages i-xx
    Download chapter PDF

  2. Introduction

    • Rosario Toscano
    Pages 1-20

  3. Stochastic Optimization Methods

    • Rosario Toscano
    Pages 21-45

  4. Heuristic Kalman Algorithm

    • Rosario Toscano
    Pages 47-69

  5. Some Notions on System Modeling

    • Rosario Toscano
    Pages 71-113

  6. Robust Control of Uncertain Parametric Systems

    • Rosario Toscano
    Pages 115-167

  7. Preventive Maintenance

    • Rosario Toscano
    Pages 169-202

  8. Machine Learning

    • Rosario Toscano
    Pages 203-238

  9. Conclusion

    • Rosario Toscano
    Pages 239-245

  10. Back Matter

    Pages 247-286
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Authors and Affiliations

  • École Nationale d’Ingénieurs de Saint-Etienne, Saint-Etienne, France

    Rosario Toscano

About the author

​Rosario Toscano was born in Catania, Italy. He received his masters degree with specialization in control from the Institut National des Sciences Appliquées de Lyon in 1996. He received the Ph.D. degree from the Ecole Centrale de Lyon in 2000. He received the HDR degree (Habilitation to Direct Research) from the University Jean Monnet of Saint-Etienne in 2007. He is currently full professor at the Ecole Nationale d'Ingénieurs de Saint-Etienne and Ecole Centrale de Lyon (ENISE-ECL). His research interests include: structured controllers, robust control, stochastic optimization methods, dynamic reliability, fault detection, multimodel approach applied to diagnosis and control, fretting wear of mechanical surfaces and sensorial design of products.

Bibliographic Information

  • Book Title: Solving Optimization Problems with the Heuristic Kalman Algorithm

  • Book Subtitle: New Stochastic Methods

  • Authors: Rosario Toscano

  • Series Title: Springer Optimization and Its Applications

  • DOI: https://doi.org/10.1007/978-3-031-52459-2

  • Publisher: Springer Cham

  • eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024

  • Hardcover ISBN: 978-3-031-52458-5Published: 22 March 2024

  • Softcover ISBN: 978-3-031-52461-5Due: 22 April 2024

  • eBook ISBN: 978-3-031-52459-2Published: 21 March 2024

  • Series ISSN: 1931-6828

  • Series E-ISSN: 1931-6836

  • Edition Number: 1

  • Number of Pages: XX, 286

  • Number of Illustrations: 1 b/w illustrations

  • Topics: Optimization, Algorithms

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