Course Meeting Times
Lectures: 2 sessions / week, 1.5 hours / session
Recitations: 1 session / week, 1 hour / session
Description
This course introduces students to the fundamentals of nonlinear optimization theory and methods. Topics include unconstrained and constrained optimization, linear and quadratic programming, Lagrange and conic duality theory, interior-point algorithms and theory, Lagrangean relaxation, generalized programming, and semi-definite programming. Algorithmic methods used in the class include steepest descent, Newton’s method, conditional gradient and subgradient optimization, interior-point methods and penalty and barrier methods.
Required Text
Bertsekas, Dimitri P. Nonlinear Programming. 2nd ed. Athena Scientific Press, 1999. ISBN: 1886529000.
Recommended Alternate Text
Bazaraa, Mokhtar S., Hanif D. Sherali, and C. M. Shetty. Nonlinear Programming: Theory and Algorithms. New York: John Wiley & Sons, 1993. ISBN: 0471557935.
Course Requirements
- Weekly Problem Sets (about 12).
- Midterm Examination (in-class, closed book).
- Final Examination (3-hour exam).
- Computer Exercises.
Grading
Grading will be based on the following:
| ACTIVITY | PERCENTAGE |
|---|---|
| Midterm Exam | 25% |
| Final Exam | 50% |
| Problem Sets | 25% |
