Pillo / Roma Large-Scale Nonlinear Optimization

1. Auflage 2006
ISBN: 978-0-387-30065-8
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 83, 298 Seiten, eBook

Reihe: Nonconvex Optimization and Its Applications

Large-Scale Nonlinear Optimization reviews and discusses recent advances in the development of methods and algorithms for nonlinear optimization and its applications, focusing on the large-dimensional case, the current forefront of much research.The chapters of the book, authored by some of the most active and well-known researchers in nonlinear optimization, give an updated overview of the field from different and complementary standpoints, including theoretical analysis, algorithmic development, implementation issues and applications.

Zielgruppe

Research

Autoren/Hrsg.

Pillo, Gianni

Roma, Massimo

Inhaltsverzeichnis

Fast Linear Algebra for Multiarc Trajectory Optimization.- Lagrange Multipliers with Optimal Sensitivity Properties in Constrained Optimization.- An O(n2) Algorithm for Isotonic Regression.- Knitro: An Integrated Package for Nonlinear Optimization.- On implicit-factorization constraint preconditioners.- Optimal algorithms for large sparse quadratic programming problems with uniformly bounded spectrum.- Numerical methods for separating two polyhedra.- Exact penalty functions for generalized Nash problems.- Parametric Sensitivity Analysis for Optimal Boundary Control of a 3D Reaction-Diffusion System.- Projected Hessians for Preconditioning in One-Step One-Shot Design Optimization.- Conditions and parametric representations of approximate minimal elements of a set through scalarization.- Efficient methods for large-scale unconstrained optimization.- A variational approach for minimum cost flow problems.- Multi-Objective Optimisation of Expensive Objective Functions with Variable Fidelity Models.- Towards the Numerical Solution of a Large Scale PDAE Constrained Optimization Problem Arising in Molten Carbonate Fuel Cell Modeling.- The NEWUOA software for unconstrained optimization without derivatives.