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Modern Numerical Nonlinear Optimization
Author
Title
Modern Numerical Nonlinear Optimization [electronic resource] / by Neculai Andrei.
ISBN
9783031087202
Edition
1st ed. 2022.
Publication
Cham : Springer International Publishing : Imprint: Springer, 2022.
Physical Description
1 online resource (XXXIII, 807 p.) 117 illus., 108 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
This book includes a thorough theoretical and computational analysis of unconstrained and constrained optimization algorithms and combines and integrates the most recent techniques and advanced computational linear algebra methods. Nonlinear optimization methods and techniques have reached their maturity and an abundance of optimization algorithms are available for which both the convergence properties and the numerical performances are known. This clear, friendly, and rigorous exposition discusses the theory behind the nonlinear optimization algorithms for understanding their properties and their convergence, enabling the reader to prove the convergence of his/her own algorithms. It covers cases and computational performances of the most known modern nonlinear optimization algorithms that solve collections of unconstrained and constrained optimization test problems with different structures, complexities, as well as those with large-scale real applications. The book is addressed to all those interested in developing and using new advanced techniques for solving large-scale unconstrained or constrained complex optimization problems. Mathematical programming researchers, theoreticians and practitioners in operations research, practitioners in engineering and industry researchers, as well as graduate students in mathematics, Ph.D. and master in mathematical programming will find plenty of recent information and practical approaches for solving real large-scale optimization problems and applications.
Variant and related titles
Springer ENIN.
Other formats
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Printed edition:
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
October 27, 2022
Series
Springer Optimization and Its Applications, 195
Contents
1. Introduction
2. Fundamentals on unconstrained optimization.-3 . Steepest descent method
4. Newton method
5. Conjugate gradient methods
6. Quasi-Newton methods
7. Inexact Newton method
8. Trust-region method
9. Direct methods for unconstrained optimization
10. Constrained nonlinear optimization methods
11. Optimality conditions for nonlinear optimization
12. Simple bound optimization
13. Quadratic programming
14. Penalty and augmented Lagrangian
15. Sequential quadratic programming
16. Generalized reduced gradient with sequential linearization. (CONOPT) - 17. Interior-point methods
18. Filter methods
19. Interior-point filter line search (IPOPT)
Direct methods for constrained optimization
20. Direct methods for constrained optimization
Appendix A. Mathematical review
Appendix B. SMUNO collection. Small scale optimization applications
Appendix C. LACOP collection. Large-scale continuous nonlinear optimization applications
Appendix D. MINPACK-2 collection. Large-scale unconstrained optimization applications
References
Author Index
Subject Index.
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