Conjugate Gradient Algorithms in Nonconvex Optimization

This up-to-date book is on algorithms for large-scale unconstrained and bound constrained optimization. Optimization techniques are shown from a conjugate gradient algorithm perspective. Large part of the book is devoted to preconditioned conjugate gradient algorithms. In particular memoryless and l...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Pytlak, Radoslaw (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2009.
Edición:1st ed. 2009.
Colección:Nonconvex Optimization and Its Applications ; 89
Temas:
Mathematical optimization.
Calculus of variations.
Operations research.
Security systems.
Calculus of Variations and Optimization.
Operations Research and Decision Theory.
Security Science and Technology.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 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.

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