### 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% |