Robust Optimization (Hardcover) (ISBN-13: 9780691143682)

Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject.

Robust optimization is designed to meet some major challenges associated with uncertainty-affected optimization problems: to operate under lack of full information on the nature of uncertainty; to model the problem in a form that can be solved efficiently; and to provide guarantees about the performance of the solution.

The book starts with a relatively simple treatment of uncertain linear programming, proceeding with a deep analysis of the interconnections between the construction of appropriate uncertainty sets and the classical chance constraints (probabilistic) approach. It then develops the robust optimization theory for uncertain conic quadratic and semidefinite optimization problems and dynamic (multistage) problems. The theory is supported by numerous examples and computational illustrations.

An essential book for anyone working on optimization and decision making under uncertainty, Robust Optimization also makes an ideal graduate textbook on the subject.

Author: Aharon Ben-Tal, Laurent El Ghaoui, Arkadi Nemirovski
ISBN-10: 0691143684
ISBN-13: 9780691143682
Publisher: Princeton University Press
Language: English
Published: 08/30/2009
Pages: 576
Format: Hardcover
Weight: 2.90lbs
Size: 10.00h x 7.20w x 1.40d

Review Citation(s):
Scitech Book News 12/01/2009 pg. 40