# Numerical Linear Algebra, July-Nov 2024

### Credits 9 credits

### Instructor Sivaram Ambikasaran,

### Classroom NAC \(504\)

### Timings E slot; T: 11AM, W: 10AM, Th: 8AM, F: 5PM

### Mailing list

You can find on google groups when you login via smail. Search for "2024_nla" or "2024_nla-group@smail.iitm.ac.in". Click on it and request to be added to the group.
### Exams and Grading

**Evaluation** |
**Points** |
**Date** |

Assignments |
20 |
Every fortnight |

Midsem |
30 |
September 27th |

EndSem |
50 |
November 21st |

### Assignments and Reading materials

Click here
- All assignments need to be submitted before Sunday midnight.
- Students are strongly encouraged to typeset their solution via LaTeX/TeX. Typesetting using LaTeX/TeX will obtain 25% bonus on the score they get for the assignment.
- Students need to submit their assignments through the dropbox link provided.
- The name of the zipped file (this should contain the LaTeX/TeX source file, pdf, and code) for submitting your assignment should be as follows: ma14c093_5.zip (or .rar or .tar), where ma14c093 is your roll number and 5 implies that you are submitting your fifth assignment.
- 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.

### Short Syllabus

Fundamentals of Matrix Algebra; Floating point arithmetic; Conditioning of a problem; Forward and backward stability of algorithms; Algorithmic complexity; Matrix decompostions: LU, Cholesky, QR, Eigendecomposition, SVD; direct and iterative techniques.
### Textbooks

- Applied Numerical Linear Algebra, by James W. Demmel
- Numerical Linear Algebra, by Nick Trefethen
- Accuracy and Stability of Numerical Algorithms by Nicholas J. Higham