Numerical Optimization with Computational Errors
This book studies the approximate solutions of optimization problems in the presence of computational errors. A number of results are presented on the convergence behavior of algorithms in a Hilbert space; these algorithms are examined taking into account computational errors. The author illustrates...
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| Format: | eBook |
| Language: | English |
| Published: |
Cham
Springer International Publishing
2016, 2016
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| Edition: | 1st ed. 2016 |
| Series: | Springer Optimization and Its Applications
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- 1. Introduction
- 2. Subgradient Projection Algorithm
- 3. The Mirror Descent Algorithm
- 4. Gradient Algorithm with a Smooth Objective Function
- 5. An Extension of the Gradient Algorithm
- 6. Weiszfeld's Method
- 7. The Extragradient Method for Convex Optimization
- 8. A Projected Subgradient Method for Nonsmooth Problems
- 9. Proximal Point Method in Hilbert Spaces
- 10. Proximal Point Methods in Metric Spaces
- 11. Maximal Monotone Operators and the Proximal Point Algorithm
- 12. The Extragradient Method for Solving Variational Inequalities
- 13. A Common Solution of a Family of Variational Inequalities
- 14. Continuous Subgradient Method
- 15. Penalty Methods
- 16. Newton's method
- References
- Index.