# 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.

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