February 22, 2012

Optimization for Engineering Design
Preface

ix. Discussion of what optimization is, how its not utilized, but it is beginning to be more common due to faster computers. Brief process description of optimization, and beginning of the different types of optimization.

x. Discusses optimization and how it is not common to have the two different types of optimization discussed in one book, and then gives a brief description of each chapter.

xi. What algorithms are listed in the book and why they are listed, and why their are hand calculations, and the goals of the book are to give a intro to students and design engineers into optimization.

xii. Basically steps listed to make the information in the book stick, follow the program review the hand calculation, work sample problems using the actual code listed, that is why all the information is given.

xiii. Acknowledgements

xiv. Acknowledgements

1. Introduction into optimization and the many different ways different types of engineers optimize certain tasks in their field.  Whether it be minimizing or maximizing both are optimizing.

2. The beginning is deciding what optimization algorithm for what design problem. Introduction as  to why a good formulation process is needed and why in many cases there are not good ones used.

3. Tells why the formulation procedure and optimization process are mathematical problem, and shows the flow chart for the optimization design process.

4. Discussion of design variables and which ones maybe important, but ultimately it depends on who the user is. They are important however, to the optimization process because certain algorithms will not work well if too many are used. Begins introduction to constraints.

5. More in depth discussion of constraints and how they may be applied to different areas of engineering and what constraints may be. Then discussion of greater then or less then constraints and how you can vary them, and further on to equality constraints and how they are harder to handle then less than or greater than constraints.

6. One way to handle equality is to do less than or equal to and then have minimum or equal to. Bottom line the less complex the constraints the smoother the optimization will be. 3rd is objective function most of time it is costs, or weight or something you can quantify however, it maybe aesthetic which is harder to handle. In almost all situations you can only pick on objective function so pick most important.

7. Further discussion of the objective function and a detailed look at an example of finding the maximizing and minimizing points for a specific function and  a way to manipulate  the function to find either the min or max by multiplying by -1. intro to variable bounds.

8. Illustration of duality principle, and discussion of the maximum and minimum variable bounds, how to choose them, and suggestions if they are correct or not.

9. Nonlinear programming problem format is shown, discussion of the problem. Further discussion of a solution to the NLP problem and what may happen in designing that algorithm, and then you get your optimal solution.