Conjugate Gradient Algorithms in Nonconvex Optimization

• Main Author: Pytlak, Radosław (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2009.
• Series: Nonconvex Optimization and Its Applications, 89
• Subjects: Mathematics.; Operations research.; Decision making.; Calculus of variations.; Quality control.; Reliability.; Industrial safety.; Calculus of Variations and Optimal Control; Optimization.; Operation Research/Decision Theory.; Quality Control, Reliability, Safety and Risk.

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...

Table of Contents:

• 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.