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

 Evaluation Date Points Assignments Due Sundays $$30$$ Quiz-I February $$14$$, Friday $$15$$ Quiz-II March $$20$$, Friday $$15$$ End Term May $$11$$, Friday $$40$$

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.

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