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MODERN NUMERICAL NONLINEAR OPTIMIZATION
- Neculai Andrei
Modern Numerical Nonlinear Optimization
Springer Optimization and Its Applications, Volume 195
Springer Science+Business Media New York 2022
ISBN: 978-3-031-08719-6
e-book ISBN: 978-3-031-08720-2
ISSN: 1931-6836
DOI: https://doi.org/10.1007/978-3-031-08720-2
Springer New York Heidelberg Dordrecht London
807 + XXXIII pages.
NECULAI ANDREI
MODERN NUMERICAL NONLINEAR OPTIMIZATION
Preface
- Contents
- List of Algorithms
- List of Figures
- List of Tables
- List of Applications
- 1. Introduction
- 2. Fundamentals on unconstrained optimization. Stepsize computation
- 3. Steepest descent methods
- 4. The Newton method
- 5. Conjugate gradient methods
- 6. Quasi-Newton methods
- 7. Inexact Newton methods
- 8. The Trust-region method
- 9. Direct methods for unconstrained optimization
- 10. Constrained nonlinear optimization methods – An overview
- 11. Optimality conditions for nonlinear optimization
- 12. Simple bound constraints optimization
- 13. Quadratic programming
- 14. Penalty and augmented Lagrangian Methods
- 15. Sequential quadratic programming
- 16. Primal methods. The generalized reduced gradient with sequential linearization
- 17. Interior-point methods
- 18. Filter methods
- 19. Interior-point filter line search
- 20. Direct methods for constrained optimization
- Appendix A: Mathematical Review
- Appendix B: The SMUNO collection. Small-scale continuous unconstrained optimization applications
- Appendix C: The LACOP collection. Large-scale continuous nonlinear optimization applications
- Appendix D: The MINPACK-2 collection. Large-scale unconstrained optimization applications
- References
- Author Index
- Subject Index