Give Your Students the Proper Groundwork for Future Studies in Optimization
A First Course in Optimization is designed for a one-semester course in optimization taken by advanced undergraduate and beginning graduate students in the mathematical sciences and engineering. It teaches students the basics of continuous optimization and helps them better understand the mathematics from previous courses.
The book focuses on general problems and the underlying theory. It introduces all the necessary mathematical tools and results. The text covers the fundamental problems of constrained and unconstrained optimization as well as linear and convex programming. It also presents basic iterative solution algorithms (such as gradient methods and the Newton-Raphson algorithm and its variants) and more general iterative optimization methods.
This text builds the foundation to understand continuous optimization. It prepares students to study advanced topics found in the author's companion book, Iterative Optimization in Inverse Problems, including sequential unconstrained iterative optimization methods.
Rezensionen / Stimmen
"This book, split in fifteen chapters, presents a thorough introduction to optimization for undergraduate and graduate courses in science and engineering and forms a useful toolkit of optimization techniques across several domains. ... Throughout this very interesting book, a number of solved and unsolved exercises provide the reader with all the necessary tools to understand the techniques and their applicability in real-life problems."
-Zentralblatt MATH 1312
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Postgraduate
Produkt-Hinweis
Fadenheftung
Gewebe-Einband
Maße
Höhe: 237 mm
Breite: 163 mm
Dicke: 25 mm
Gewicht
ISBN-13
978-1-4822-2656-0 (9781482226560)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Optimization without Calculus. Geometric Programming. Basic Analysis. Convex Sets. Vector Spaces and Matrices. Linear Programming. Matrix Games and Optimization. Differentiation. Convex Functions. Convex Programming. Iterative Optimization. Solving Systems of Linear Equations. Conjugate-Direction Methods. Operators. Looking Ahead. Bibliography. Index.
