The objective of this course is to give graduate students introduction to nonlinear programming (specifically focusing on convex optimization). Students are expected to have proper knowledge in linear algebra, calculus, linear programming and other basic OR techniques which are normally expected to be covered in the undergraduate operations research curriculum.

MATH110 Calculus, MATH112 Applied Linear Algebra, IMEN261 Introduction to Operations Research or equivalent

Attendance 10%, Homework 0%, Midterm 40%, Final 50%

Bertsekas, D. P. Nonlinear programming, 2nd edition, 1999
Chong, E. K. P. and Zak, S. H., An Introduction to Optimization, 2nd edition, 2004
Bazaraa, M. S., Sherali, H. D., Shetty, C. M., Nonlinear Programming Theory and Algorithms, 3rd edition, 2006

Week 1-8 Chapter 1-5: Theory on Convex Optimization

Week 9-11 Chapter 7-8: Statistical estimation, Geometric problems

Week 12-13 Chapter 10: Unconstrained Optimization

Week 14-16 Chapter 11: Constrained Optimization

Roughly, homework assignments will be assigned when a chapter finishes. They are not collected. The problems in them are for students’ practice, and some of them will appear in the midterm and the final exams.

There will be two exams for this course. Exams may or may not be cumulative. A university excused absence is required to make up a missed exam and arrangements should be made prior to the testing day. Other instructions regarding exams will be given in class.

Academic integrity is one of the most important aspects in education. Violating academic integrity will NOT be allowed in any case and result in failure (F grade) in this course.