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Optimization in Practice with MATLAB® provides a unique approach to optimization education. It is accessible to both junior and senior undergraduate and graduate students, as well as industry practitioners. It provides a strongly practical perspective that allows the student to be ready to use optimization in the workplace. It covers traditional materials, as well as important topics previously unavailable in optimization books (e.g. numerical essentials - for successful optimization). Written with both the reader and the instructor in mind, Optimization in Practice with MATLAB® provides practical applications of real-world problems using MATLAB®, with a suite of practical examples and exercises that help the students link the theoretical, the analytical, and the computational in each chapter. Additionally, supporting MATLAB® m-files are available for download via www.cambridge.org.messac. Lastly, adopting instructors will receive a comprehensive solution manual with solution codes along with lectures in PowerPoint with animations for each chapter, and the text´s unique flexibility enables instructors to structure one- or two-semester courses.

The realistic examples differentiate this book from others on the market
Offers a wide-ranging collection of examples and exercises, providing a unique platform for linking the theoretical, computational, and practical
A MATLAB® chapter and prerequisite math chapter address diverse students´ preparation levels
A robust ancillary support package includes comprehensive PowerPoint lectures with animations, a comprehensive solutions manual with solution codes, and MATLAB® codes for examples and solutions



Table of Contents

Part I. Helpful Preliminaries:
1. MATLAB® as a computational tool
2. Mathematical preliminaries
Part II. Using Optimization - the Road Map:
3. Welcome to the fascinating world of optimization
4. Analysis, design, optimization, and modeling
5. Introducing linear and nonlinear programming
Part III. Using Optimization - Practical Essentials:
6. Multiobjective optimization
7. Numerical essentials
8. Global optimization basics
9. Discrete optimization basics
10. Practicing optimization - larger examples
Part IV. Going Deeper: Inside the Codes and Theoretical Aspects:
11. Linear programming
12. Nonlinear programming with no constraints
13. Nonlinear programming with constraints
Part V. More Advanced Topics in Optimization:
14. Discrete optimization
15. Modeling complex systems: surrogate modeling and design space reduction
16. Design optimization under uncertainty
17. Methods for Pareto frontier generation/representation
18. Physical programming for multiobjective optimization
19. Evolutionary algorithm