Large-Scale Nonlinear Optimization [electronic resource] / edited by G. Di Pillo, M. Roma. - X, 298 p. 44 illus. online resource. - Nonconvex Optimization and Its Applications, 83 1571-568X ; . - Nonconvex Optimization and Its Applications, 83 .

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.

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. Audience This book is intended for researchers in applied mathematics, advanced engineering, and computer science; it is also recommended for further reading within graduate studies, postgraduate and doctoral programs.


10.1007/0-387-30065-1 doi

Mathematical optimization.
Operations research.
Management science.
Science, general.
Operations Research, Management Science.



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