Optimization in Practice with MATLAB: For Engineering Students and Professionals

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.

Table of Contents

Part I. Helpful Preliminaries
Chapter 1 Matlab® As A Computational Tool
Chapter 2 Mathematical Preliminaries

Part II. Using Optimization—The Road Map
Chapter 3 Welcome To The Fascinating World Of Optimization
Chapter 4 Analysis, Design, Optimization And Modeling
Chapter 5 Introducing Linear And Nonlinear Programming
Chapter Part Iii. Using Optimization—Practical Essentials
Chapter 6 Multiobjective Optimization
Chapter 7 Numerical Essentials
Chapter 8 Global Optimization Basics
Chapter 9 Discrete Optimization Basics
Chapter 10 Practicing Optimization—Larger Examples

Part IV. Going Deeper: Inside The Codes And Theoretical Aspects
Chapter 11 Linear Programming
Chapter 12 Nonlinear Programming With No Constraints
Chapter 13 Nonlinear Programming With Constraints

Part V. More Advanced Topics In Optimization
Chapter 14 Discrete Optimization
Chapter 15 Modeling Complex Systems: Surrogate Modeling And Design Space Reduction
Chapter 16 Design Optimization Under Uncertainty
Chapter 17 Methods For Pareto Frontier Generation/Representation
Chapter 18 Physical Programming For Multiobjective Optimization
Chapter 19 Evolutionary Algorithms