E-Book, Englisch, Band 89, 478 Seiten, eBook
Pytlak Conjugate Gradient Algorithms in Nonconvex Optimization
2009
ISBN: 978-3-540-85634-4
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark
E-Book, Englisch, Band 89, 478 Seiten, eBook
Reihe: Nonconvex Optimization and Its Applications
ISBN: 978-3-540-85634-4
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark
This book details algorithms for large-scale unconstrained and bound constrained optimization. It shows optimization techniques from a conjugate gradient algorithm perspective as well as methods of shortest residuals, which have been developed by the author.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
Conjugate Direction Methods for Quadratic Problems.- Conjugate Gradient Methods for Nonconvex Problems.- Memoryless Quasi-Newton Methods.- Preconditioned Conjugate Gradient Algorithms.- Limited Memory Quasi-Newton Algorithms.- The Method of Shortest Residuals and Nondifferentiable Optimization.- The Method of Shortest Residuals for Differentiable Problems.- The Preconditioned Shortest Residuals Algorithm.- Optimization on a Polyhedron.- Conjugate Gradient Algorithms for Problems with Box Constraints.- Preconditioned Conjugate Gradient Algorithms for Problems with Box Constraints.- Preconditioned Conjugate Gradient Based Reduced-Hessian Methods.
