Introduction to Applied Optimization: 22 (Springer Optimization and Its Applications)

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9781441945709: Introduction to Applied Optimization: 22 (Springer Optimization and Its Applications)

From reviews:

"The book is well written and the presentation is rigorous and self-contained. The book will be of interest to researchers in various fields as well as undergraduate and graduate students in engineering sciences, management science, and decision science.”
—I. M. Stancu-Minasian, Zentralblatt MATH, Vol. 1043 (18), 2004

"Urmila Diwekar’s book on applied optimization is one of the few books on the subject that combines impressive breadth of coverage with delightful readability. In her exposition of concepts and algorithms in the major areas of optimization, she always goes to the heart of the matter and illustrates her explanations with simple diagrams and numerical examples. Graduate and undergraduate students, who constitute part of the target audience, should find this a very useful book.”
—Jamshed A. Modi, Interfaces, Vol. 36 (1), 2006

"The book is well-written, and . . . it covers fairly significant territory, in a relatively slim volume. This work is definitely a welcome addition to the existing optimization literature, given its emphasis on modeling and solution practice, as well as its ‘user-friendly’ style of exposition.”
—János D. Pintér, European Journal of Operations Research, Vol. 177, 2007

From the reviews of the second edition:

"Diwekar’s book covers a wide variety of optimization topics, including some recent developments, in under 300 pages. … The book would be suitable as a textbook; it contains numerous exercises and a solutions manual is available." (John D. Cook, MAA Online, February, 2009)

Vom Verlag:

  • Provides well-written self-contained chapters, including problem sets and exercises, making it ideal for the classroom setting;
  • Introduces applied optimization to the hazardous waste blending problem;
  • Explores linear programming, nonlinear programming, discrete optimization, global optimization, optimization under uncertainty, multi-objective optimization, optimal control and stochastic optimal control;
  • Includes an extensive bibliography at the end of each chapter and an index;
  • GAMS files of case studies for Chapters 2, 3, 4, 5, and 7 are linked to;

          Solutions manual available upon adoptions.

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