Convex Optimization with Computational Errors
This book studies approximate solutions of optimization problems in the presence of computational errors. It contains a number of results on the convergence behavior of algorithms in a Hilbert space, which are well known as important tools for solving optimization problems. The research presented co...
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| Format: | eBook |
| Language: | English |
| Published: |
Cham
Springer International Publishing
2020, 2020
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| Edition: | 1st ed. 2020 |
| 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:
- Preface
- 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. Continuous Subgradient Method
- 7. An optimization problems with a composite objective function
- 8. A zero-sum game with two-players
- 9. PDA-based method for convex optimization
- 10 Minimization of quasiconvex functions.-11. Minimization of sharp weakly convex functions.-12. A Projected Subgradient Method for Nonsmooth Problems
- References. -Index.