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Hoffmann / Forst

Optimization-Theory and Practice

Medium: Buch
ISBN: 978-1-4939-3933-6
Verlag: Springer
Erscheinungstermin: 23.08.2016
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Optimization is a field important in its own right but is also integral to numerous applied sciences, including operations research, management science, economics, finance and all branches of mathematics-oriented engineering. Constrained optimization models are one of the most widely used mathematical models in operations research and management science.

This book gives a modern and well-balanced presentation of the subject, focusing on theory but also including algorithims and examples from various real-world applications. The text is easy to read and accessible to anyone with a knowledge of multi-dimensional calculus, linear algebra and basic numerical methods. Detailed examples and counter-examples are provided - as are exercises, solutions and helpful hints, and Matlab/Maple supplements.

The intended readership is advanced undergraduates, graduates, and professionals in any of the applied fields.

Produkteigenschaften

  • Artikelnummer: 9781493939336
  • Medium: Buch
  • ISBN: 978-1-4939-3933-6
  • Verlag: Springer
  • Erscheinungstermin: 23.08.2016
  • Sprache(n): Englisch
  • Auflage: Softcover Nachdruck of the original 1. Auflage 2010
  • Serie: Springer Undergraduate Texts in Mathematics and Technology
  • Produktform: Kartoniert, Previously published in hardcover
  • Gewicht: 1087 g
  • Seiten: 402
  • Format (B x H x T): 178 x 254 x 23 mm
  • Ausgabetyp: Kein, Unbekannt

Themen

Autoren/Hrsg.

Autoren

1. Introduction: Examples of Optimization Problems, Historical
Overview.- 2. Optimality Conditions: Convex Sets, Inequalities, Local
First- and Second-Order Optimality Conditions, Duality.- 3. Unconstrained Optimization Problems: Elementary Search and Localization Methods, Descent Methods with Line Search, Trust Region Methods, Conjugate Gradient Methods, Quasi-Newton Methods.- 4. Linearly Constrained Optimization Problems: Linear and Quadratic Optimization, Projection Methods.- 5. Nonlinearly Constrained Optimization Methods: Penalty Methods, SQP Methods.- 6. Interior-Point Methods for Linear Optimization: The Central Path, Newton's Method for the Primal-Dual System, Path-Following Algorithms, Predictor-Corrector Methods.- 7. Semidefinite Optimization:

Selected Special Cases, The S-Procedure, The Function log°det, Path-Following Methods, How to Solve SDO Problems?, Icing on the
Cake: Pattern Separation via Ellipsoids.- 8. Global Optimization:

Branch and Bound Methods, Cutting Plane Methods.- Appendices:

A Second Look at the Constraint Qualifications, The Fritz John Condition, Optimization Software Tools for Teaching and Learning.-
Bibliography.- Index of Symbols.- Subject Index.