Conjugate gradient algorithms in nonconvex optimization [electronic resource] / Radosław Pytlak.
Explains algorithms for large-scale unconstrained and bound constrained optimization. This book shows optimization techniques from a conjugate gradient algorithm perspective. It is devoted to preconditioned conjugate gradient algorithms. It focuses on the methods of shortest residuals developed by t...
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| Online Access: |
Full Text (via Springer) |
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| Main Author: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Berlin :
Springer,
©2009.
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| Series: | Nonconvex optimization and its applications ;
v. 89. |
| Subjects: |
Table of Contents:
- Conjugate directions methods for quadratic problems
- Conjugate gradient methods for nonconvex problems
- Memoryless quasi-Newton methods
- Preconditioned conjugate gradient algorithms
- Limited memory quasi-Newton algorithms
- A method of shortest residuals and nondifferentiable optimization
- The method of shortest residuals for smooth problems
- The preconditioned shortest residuals algorithm
- Optimization on a polyhedron
- Problems with box constraints
- The preconditioned shortest residuals algorithm with box
- Conjugate gradient reduced-Hessian method
- Elements of topology and analysis
- Elements of linear algebra.