Introduction to Methods for Nonlinear Optimization
This book has two main objectives: • to provide a concise introduction to nonlinear optimization methods, which can be used as a textbook at a graduate or upper undergraduate level; • to collect and organize selected important topics on optimization algorithms, not easily found in textbooks, which c...
সংরক্ষণ করুন:
| প্রধান লেখক: | , |
|---|---|
| সংস্থা লেখক: | |
| বিন্যাস: | বৈদ্যুতিক বৈদ্যুতিন গ্রন্থ |
| ভাষা: | ইংরেজি |
| প্রকাশিত: |
Cham :
Springer International Publishing : Imprint: Springer,
2023.
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| সংস্করন: | 1st ed. 2023. |
| মালা: | La Matematica per il 3+2,
152 |
| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | https://doi.org/10.1007/978-3-031-26790-1 |
| ট্যাগগুলো: |
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সূচিপত্রের সারণি:
- 1 Introduction.-2 Fundamental definitions and basic existence results
- 3 Optimality conditions for unconstrained problems in Rn
- 4 Optimality conditions for problems with convex feasible set
- 5 Optimality conditions for Nonlinear Programming
- 6 Duality theory
- 7 Optimality conditions based on theorems of the alternative
- 8 Basic concepts on optimization algorithms
- 9 Unconstrained optimization algorithms
- 10 Line search methods
- 11 Gradient method
- 12 Conjugate direction methods
- 13 Newton’s method
- 14 Trust region methods
- 15 Quasi-Newton Methods
- 16 Methods for nonlinear equations
- 17 Methods for least squares problems
- 18 Methods for large-scale optimization
- 19 Derivative-free methods for unconstrained optimization
- 20 Methods for problems with convex feasible set
- 21 Penalty and augmented Lagrangian methods
- 22 SQP methods
- 23 Introduction to interior point methods
- 24 Nonmonotone methods
- 25 Spectral gradient methods
- 26 Decomposition methods
- Appendix A: basic concepts of linear algebra and analysis
- Appendix B: Differentiation in Rn
- Appendix C: Introduction to convex analysis.