EE 5037 Linear and Nonlinear Programming

Fall 2024

Course Staff

Instructor:

Jay Cheng (鄭  傑), Delta 811 (台達館 811), ext. 62207.

Lectures: T7T8R7, Delta 212 (台達館 212).

Office Hours: T9R8, after classes, or by appointment.

Teaching Assistants:

黃信翔 (whu910116hung1009@gmail.com)

EECS 805 (資電館805), ext. 34154

TAs' Office Hours: R4

Announcements

The following schedule is subject to change as the course progresses, and you should come back frequently for the most recent update.
  1. Week 1: Chapter 1, pp. 1~11
  2. Week 2: Chapter 1, pp. 12~27
  3. Week 3: Chapter 1, pp. 28~40
  4. Week 4: Chapter 1, pp. 41~55
  5. Week 5: Chapter 1, pp. 56~71
  6. Week 6: Chapter 1, pp. 72~87
  7. Week 7: Chapter 2, pp. 1~20
  8. Week 8: Chapter 2, pp. 21~41
  9. Week 9: Chapter 2, pp. 42~60
  10. Week 10: Midterm 1
  11. Week 11: Chapter 2, pp. 61~80
  12. Week 12: Chapter 3, pp. 1~21
  13. Week 13: Chapter 4, pp. 1~17
  14. Week 14: Midterm 2
  15. Week 15: Chapter 4, pp. 18~29
  16. Week 16: Chapter 4, pp. 30~44

Textbook

1. Roger Webster, Convexity. New York, NY: Oxford University Press, 1994.

2. David G. Luenberger and Yinyu Ye, Linear and Nonlinear Programming, 3rd ed. New York, NY: Springer, 2008.

References

1. Leonard D. Berkovitz, Convexity and Optimization in R^n. New York, NY: John wiley & Sons, 2002.

2. David G. Luenberger, Optimization by Vector Space Method. New York, NY: John wiley & Sons, 1969.

3. R. Tyrrell Rockafellar, Convex Analysis. Princeton, NJ: Princeton University Press, 1997.

4. Alexander Barvinok, A Course in Convexity. American Mathematical Society, 2002.

5. Branko Grünbaum, Convex Polytopes, 2nd ed. New York, NY: Springer-Verlag, 2003.

Course Descriptions

This course is an introduction to linear and nonlinear programming and its applications.

Syllabus

1. The Euclidean Space R^n

2. Convex Sets

3. Convex Polytopes

4. Linear Programming

Grading

Your grade = Exam (25%x2=50%) + In-class Q&A and presentation (50%)

Go to Jay's Homepage