Practical Mathematical Optimization. An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms.

Snyman, Jan A.

ISBN 10: 0387243488 / ISBN 13: 9780387243481

Verlag: New York, Springer., 2005

Beschreibung

XX, 257p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Stamped/gestempelt. Applied Optimization , Vol. 97. Sprache: Englisch.

Bestandsnummer des Verkäufers 35643HB

Bibliografische Details

• Titel: Practical Mathematical Optimization. An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms.
• Verlag: New York, Springer.
• Erscheinungsjahr: 2005
• Sprache: Englisch
• ISBN-10: 0387243488
• ISBN-13: 9780387243481
• Einband: Hardcover
• Verkäuferkataloge:
  • Mathematik

Über diesen Titel

Inhaltsangabe

This book presents basic optimization principles and gradient-based algorithms to a general audience, in a brief and easy-to-read form. It enables professionals to apply optimization theory to engineering, physics, chemistry, or business economics.

Von der hinteren Coverseite

This book presents basic optimization principles and gradient-based algorithms to a general audience, in a brief and easy-to-read form without neglecting rigour. The work should enable the professional to apply optimization theory and algorithms to his own particular practical field of interest, be it engineering, physics, chemistry, or business economics. Most importantly, for the first time in a relatively brief and introductory work, due attention is paid to the difficulties such as noise, discontinuities, expense of function evaluations, and the existence of multiple minima that often unnecessarily inhibit the use of gradient-based methods. In a separate chapter on new gradient-based methods developed by the author and his coworkers, it is shown how these difficulties may be overcome without losing the desirable features of classical gradient-based methods.

 Audience

It is intended that this book be used in senior- to graduate-level semester courses in optimization, as offered in mathematics, engineering, computer science, and operations research departments, and also to be useful to practising professionals in the workplace.