This volume presents its author's pioneering stochastic approach to global optimization across a range of disciplines, including mathematics and engineering. The new method confronts constrained and unconstrained problems and unifies our conceptual framework.

This self-contained monograph presents a new stochastic approach to global optimization problems arising in a variety of disciplines including mathematics, operations research, engineering, and economics. The volume deals with constrained and unconstrained problems and puts a special emphasis on large scale problems. It also introduces a new unified concept for unconstrained, constrained, vector, and stochastic global optimization problems. All methods presented are illustrated by various examples. Practical numerical algorithms are given and analyzed in detail. The topics presented include the randomized curve of steepest descent, the randomized curve of dominated points, the semi-implicit Euler method, the penalty approach, and active set strategies. The optimal decoding of block codes in digital communications is worked out as a case study and shows the potential and high practical relevance of this new approach. Global Optimization: A Stochastic Approach is an elegant account of a refined theory, suitable for researchers and graduate students interested in global optimization and its applications.

Presents a stochastic approach to the dynamic research field of global optimization Covers relevant prerequisites from differential geometry and probability theory Reinforces theory through the necessary motivation and numerical results Includes supplementary material: sn.pub/extras

Autorentext

Prof. Dr. Dr. Stefan Schäffler ist Ordinarius für Mathematik und Operations Research an der Universität der Bundeswehr München.

Inhalt
Preface.- Introduction.- Preliminaries.- The Approach.- Theoretical Results.- The Algorithm.- Numerical Results.- References.- Index.