Nonlinear Conjugate Gradient Methods for Unconstrained Optimization
For each method, the convergence analysis, the computational performances and the comparisons versus other conjugate gradient methods are given. The theory behind the conjugate gradient algorithms presented as a methodology is developed with a clear, rigorous, and friendly exposition; the reader wil...
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| Format: | eBook |
| Language: | English |
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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:
- 1. Introduction
- 2. Linear Conjugate Gradient Algorithm
- 3. General Convergence Results for Nonlinear Conjugate Gradient Methods
- 4. Standard Conjugate Gradient Methods
- 5. Acceleration of Conjugate Gradient Algorithms
- 6. Hybrid and Parameterized Conjugate Gradient Methods
- 7. Conjugate Gradient Methods as Modifications of the Standard Schemes
- 8. Conjugate Gradient Methods Memoryless BFGS Preconditioned
- 9. Three-Term Conjugate Gradient Methods
- 10. Other Conjugate Gradient Methods
- 11. Discussion and Conclusions
- References
- Author Index
- Subject Index