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Full Description
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.
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 algorithms.
