Introduction to Linear Optimization and Extensions with MATLAB
Roy H. Kwon, University of Toronto
CRC Press, Inc., 2014
ISBN: 978-1-4398-6264-3;
Language: English
Filling the need for an introductory book on linear programming that discusses methods used to mitigate parameter uncertainty, Introduction to Linear Optimization and Extensions with MATLAB provides a concrete and intuitive introduction to modern linear optimization. In addition to fundamental topics, the book discusses current linear optimization technologies such as predictor-path following interior point methods for both linear and quadratic optimization, as well as the inclusion of linear optimization of uncertainty (example: stochastic programming with recourse and robust optimization).
The author introduces both stochastic programming and robust optimization as frameworks to deal with parameter uncertainty. The book follows an unusual approach—developing these topics in an introductory book—which highlights their importance. Since most applications require decisions to be made in the face of uncertainty, the early introduction of these topics facilitates decision making in real world environments. The author also includes applications and case studies from finance and supply chain management that involve the use of MATLAB.
Even though there are several LP texts in the marketplace, most do not cover data uncertainty using stochastic programming and robust optimization techniques. Most emphasize the use of Microsoft Excel®, while this book uses MATLAB, which is the primary tool of many engineers, including financial engineers. The book focuses on state-of-the-art methods for managing parameter uncertainty in linear programming, rigorously developing theory and methods. The meticulous attention used to develop intuition before presenting theory makes the material come alive.
In addition, a supplemental set of MATLAB code files are available for download.
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