SEDS 534
Optimization Methods
Vector spaces and matrices, elements of calculus, Unconstrained optimization, one-dimensional search method, golden section, Fibonacci, Newton s method, gradient search methods, Steepest-descent, Newton s method, conjugate-gradient, Least squares analysis, linear programming, heuristic optimization methods, simulated annealing
Course Objectives
To introduce the fundamentals of various optimization methods and latest research.
Recommended or Required Reading
E. K. P. Chong and S. H. Zak, An Introduction to Optimization, Third Edition, New York, NY: John Wiley & Sons, Inc. (Wiley-Interscience Series), 2008
Learning Outcomes
1. To demonstrate the ability to formulate and solve engineering problems
2. To classify the various optimization methods
3. To be able to create a technique for a specific problem
4. To be able to use optimization tools
| Week | Topics |
|---|---|
| 1 | Introduction to optimization |
| 2 | Mathematical Review I: Vectors and Matrices |
| 3 | Mathematical Review II: Calculus |
| 4 | Unconstrained optimization |
| 5 | Golden Section, Fibonacci, Newton’s method |
| 6 | Gradient Search Methods and Least Squares |
| 7 | Constrained Optimization |
| 8 | Linear Programming |
| 9 | Evaluation and Review |
| 10 | Heuristic Optimization Methods |
| 11 | Neural Networks, Simulated Annealing |
| 12-14 | Term Project Presentations and Discussions |
Grading
Midterm 30%
Research Presentation 30%
Final 40%