Numerical Optimization, January-May 2020
Credits 9 credits
When F slot (Tuesday, Wednesday, Thursday, Friday)
Where New Academic Complex, Room 506
Instructor
Sivaram Ambikasaran, 
Office hours: By appointment at NAC 648
Exams and Grading
| Evaluation |
Date |
Points |
| Assignments |
Due Sundays |
\(30\) |
| Quiz-I |
February \(14\), Friday |
\(15\) |
| Quiz-II |
March \(20\), Friday |
\(15\) |
| End Term |
May \(11\), Friday |
\(40\) |
Gradebook
Assignments
- All assignments due on Sundays before 11:59 PM.
- Late assignments will be marked zero.
- There will be both written and computational part in the assignments.
- Students need to submit their assignments in IPython/Jupyter notebooks through the dropbox link provided.
- The name of the Python notebook should be as follows: ma14m093_5.ipynb, where ma14m093 is your roll number and 5 implies that you are submitting your fifth assignment.
- Students need to submit their assignments in IPython/Jupyter notebooks through the dropbox link provided.
- Any copying on assignments will result in a zero on the assignment.
- We will be using JPlag to detect similarities among multiple sets of source code files.
- The grader will expect you to express your ideas clearly, legibly, and completely, often requiring complete English sentences rather than merely just a long string of equations or unconnected mathematical expressions. This means you could lose points for poorly written proofs or answers. Clear exposition is a crucial ingredient of technical communication. Clarity of thought and presentation is more important in mathematics & sciences than any other field. The only way to master exposition is by repeated practicing.
Setting up Jupyter notebook
Assignment 1 due on Jan 26 A sample jupyter notebook file for first assignment is here.
Combined Assignments
LINK FOR ASSIGNMENT SUBMISSIONS
Lecture summary
First class will be on Jan 14, 2020, Tuesday.
Lecture summary
Short Syllabus
- Unconstrained optimization: Line Search methods, Trust region methods, Descent methods, Conjugate direction methods, Newton methods;
- Constrained Optimization: Linear Programming: Simplex Method; Linear Programming: Interior Point Methods; Non-linear constrained optimization, Quadratic programming, Penalty, Barrier and Augmented Lagrangian methods, Sequential quadratic programming
Textbooks
Other textbooks
Online lectures
Please watch the online lectures from this website as per the schedule given below.
- March 24: Module 7 Lecture 20
- March 25: Module 7 Lecture 21
- March 26: Module 7 Lecture 22
- March 27: Module 7 Lecture 23
- March 31: Module 7 Lecture 24
- April 1: Module 7 Lecture 25
- April 2: Module 7 Lecture 26
- April 3: Module 8 Lecture 27
- April 7: Module 8 Lecture 28
- April 8: Module 8 Lecture 29
- April 9: Module 9 Lecture 30
- April 10: Module 9 Lecture 31
- April 14: Module 9 Lecture 32
- April 15: Module 9 Lecture 33
- April 16: Module 9 Lecture 34
- April 17: Module 9 Lecture 35
- April 21: Module 9 Lecture 36
- April 22: Module 9 Lecture 37
- April 23: Module 10 Lecture 38
- April 24: Module 10 Lecture 39
- April 28: Module 10 Lecture 40
- April 29: Module 10 Lecture 41