Lectures: 2 sessions / week, 1.5 hours / session
Recitations: 1 session / week, 1 hour / session
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.
Bertsekas, Dimitri P. Nonlinear Programming. 2nd ed. Athena Scientific Press, 1999. ISBN: 1886529000.
Bazaraa, Mokhtar S., Hanif D. Sherali, and C. M. Shetty. Nonlinear Programming: Theory and Algorithms. New York: John Wiley & Sons, 1993. ISBN: 0471557935.
Grading will be based on the following:
| ACTIVITY | PERCENTAGE |
|---|---|
| Midterm Exam | 25% |
| Final Exam | 50% |
| Problem Sets | 25% |
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