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
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.
Preface.- Introduction.- Preliminaries.- The Approach.- Theoretical Results.- The Algorithm.- Numerical Results.- References.- Index
From the reviews:
"This book includes a well-written and structured state-of-the-art survey, which gives the interested reader, both practitioner and researcher, essential information on what is necessary for global optimization. The book also provides information on recent and ongoing scientific investigations worldwide; thus, it invites readers to do their own scientific studies. ... We believe that both today´s and future generations of students, teachers, researchers, and industry representatives could benefit from this book.” (Miray Hanim (Aslan) Yildirim and Gerhard-Wilhelm Weber, Interfaces, Vol. 44 (1), January-February, 2014)
"Introducing stochastic methods, the author presents an elegant and widely applicable new approach to global optimization, constrained or unconstrained, scalar or vector, with special emphasis on large scale problems. ... Practical numerical methods are discussed in detail. Numerous explicit examples and problems are given. ... Due to three appendices summarizing the tools from probability, the book is self-contained ... for the reader familiar with some basics of initial value problems and classical local optimization.” (Heinrich Hering, Zentralblatt MATH, Vol. 1262, 2013)
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.
Preface.- Introduction.- Preliminaries.- The Approach.- Theoretical Results.- The Algorithm.- Numerical Results.- References.- Index.
From the reviews:
"This book includes a well-written and structured state-of-the-art survey, which gives the interested reader, both practitioner and researcher, essential information on what is necessary for global optimization. The book also provides information on recent and ongoing scientific investigations worldwide; thus, it invites readers to do their own scientific studies. ... We believe that both today's and future generations of students, teachers, researchers, and industry representatives could benefit from this book." (Miray Hanim (Aslan) Yildirim and Gerhard-Wilhelm Weber, Interfaces, Vol. 44 (1), January-February, 2014)
"Introducing stochastic methods, the author presents an elegant and widely applicable new approach to global optimization, constrained or unconstrained, scalar or vector, with special emphasis on large scale problems. ... Practical numerical methods are discussed in detail. Numerous explicit examples and problems are given. ... Due to three appendices summarizing the tools from probability, the book is self-contained ... for the reader familiar with some basics of initial value problems and classical local optimization." (Heinrich Hering, Zentralblatt MATH, Vol. 1262, 2013)
Über den Autor
Prof. Dr. Dr. Stefan Schaffler, Universitat der Bundeswehr Munchen, Fakultat fur Elektrotechnik und Informationstechnik, Mathematik und Operations Research, EIT 1, Neubiberg"
Inhaltsverzeichnis
Preface.- Introduction.- Preliminaries.- The Approach.- Theoretical Results.- The Algorithm.- Numerical Results.- References.- Index
Klappentext
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. n nThe 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.n nGlobal 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
