SYST 417: OPTIMIZATION METHODS IN SYSTEMS ENGINEERING

Fall 2001

tel: 703 993 1658 email: tfriesz@gmu.edu

**HOMEWORK:**
Homework will be assigned and randomly graded; homework will be the primary
source of questions for the exams. Following the due dates for homework
assignments, solutions of most homework problems will be presented during the
lectures.

**EXAMS:** Two
midterms and a final exam. The exams will be in-class exams. See “Special
Information” below.

**PROJECT:** Every
student will be required to do a term paper or an approved project. The nature
and details of this requirement will be described during the first lecture.

**GRADING:** Your
homework will constitute 25% of you grade; each of the three exams will
constitute 25%.

**RECOMMENDED TEXT:**
The following text is recommended:

1.
Bhatti: *Practical
Optimization Methods*, Springer, 2000 (isbn 0 387 98631 6)

**RECOMMENDED SOFTWARE:** You must acquire and learn how to use the
linear and nonlinear mathematical programming utilities contained in __one__
of the following commercial software packages:

** **

1.
GAMS

2.
MATLAB

3.
AMPL

4.
MAPLE

5.
Scientific Work
Place (http://www.mackichan.com)

**IMPRORTANCE OF
LECTURES:** Lectures will be the single most important source of information.
The book will be used for suggested readings to reinforce the classroom
presentation and sometimes as a source of homework, exam problems and the term
paper/project. Although there are no explicit penalties for missing a lecture,
experience has shown that students who miss the lectures are unable to fully
comprehend the material and do extremely poorly on exams. Much of each
classroom presentation is extemporaneous and geared to the particular
difficulties of the class on the day of the lecture; as a consequence notes are
of great importance.

PREVIOUS COURSE EVALUATIONS: In
Fall 1998 this course received an overall rating of 4.94; in Fall 1999 the
rating was 4.65; in Spring 2001 the rating was 4.85. A summary of student
evaluations may be found at

OUTLINE

1. Introduction

2. Definition of a Mathematical Program

3. Optimality Defined

4. Necessary and Sufficient Conditions

5. Algorithms for Unconstrained Nonlinear Programs

6. Linear Programming Algorithms

7. Algorithms for Constrained Nonlinear Programs

8. Case Studies in Applications of Mathematical Programming

9. Equilibrium Problems

10. Case Studies in Equilibrium Modeling

11. Optimal Control Problem Defined

12. Necessary Conditions for Deterministic Optimal Control: The Minimum Principle

13. Optimal Control Case Studies