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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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Main Author: | |
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Corporate Author: | |
Format: | eBook |
Language: | English |
Published: |
Switzerland :
Springer,
2016.
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Series: | Springer optimization and its applications ;
v. 108. |
Physical Description: |
1 online resource (ix, 304 pages). |
Subjects: | |
Online Access: | SpringerLink - Click here for access |
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.