Basic Concepts of Nonlinear Optimization

This textbook is an introduction to nonlinear optimization, which treats mathematical concepts stringently on the one hand, but also motivates them in great detail and illustrates them with 42 figures. Therefore, the book is not only aimed at mathematicians, but also at natural scientists, engineers, and economists who want to understand and apply mathematically sound methods in their field.

With just over two hundred pages, the book offers sufficient flexibility to serve as a foundation for various types of lectures on nonlinear optimization. Many geometric approaches for understanding both optimality conditions and numerical methods introduce a new perspective, enriching the existing literature on optimization. This is particularly evident in the detailed treatment of issues arising from different functional descriptions of the same geometry of feasible sets, and the thus motivated introduction of constraint qualifications for deriving derivative-based optimality conditions.

This book is the English translation of the second edition of Grundzüge der Nichtlinearen Optimierung (Springer, 2021) written in German. The translation was done with the help of artificial intelligence. A subsequent revision was performed by the author to further refine the work and to ensure that the translation is appropriate concerning content and scientific correctness. It may, however, read stylistically different from a conventional translation.

A comprehensible and application-driven introduction to nonlinear optimization Contains numerous examples from theory and practice Especially suitable for non-mathematicians

Auteur

Prof. Dr. Oliver Stein is a full professor at the Karlsruhe Institute of Technology, where he heads the Continuous Optimization group at the Institute for Operations Research. His research focuses on the design and implementation of optimization methods and their theoretical foundations. His teaching focuses on global optimization, nonlinear optimization, mixed-integer optimization, convex analysis, parametric optimization, and multiobjective optimization.

Contenu

Introduction.- Unconstrained Optimization.- Constrained Optimization.