Numerical Analysis (Aug-Dec 2018)
Class timings
- Tuesday: 9 am – 10:30 am
- Friday: 9 am – 10:30 am
Grading
- Assignments : 30%
- Mid-term exam : 35%
- Final exam : 35%
Additionally
- You can submit your assignments as latex, jupyter notebook or hand written.
- Cleanliness and clarity in writing will carry some weightage, for assignments and tests.
Examinations
- Mid term exam: Friday 28 September, 9am to 12:30pm
- Final exam:
References
- K. Atkinson, Introduction to Numerical Analysis, Wiley
- David Kincaid and E. Ward Cheney, Numerical Analysis: Mathematics of Scientific Computing
- Robert Corless and Nicolas Fillion, A Graduate Introduction to Numerical Methods
- Lloyd N. Trefethen, Approximation Theory and Approximation Practice
- James W. Demmel, Applied Numerical Linear Algebra
- Gene H. Golub and Charles F. Van Loan, Matrix Computations
- Youcef Saad, Iterative Methods for Sparse Linear Systems PDF
Introduction to Python
You can get the notebooks using git
git clone https://github.com/cpraveen/python
This will download all the code inside the directory called python. Alternately, you can download a zip file containing all the code here
You can also read the codes and notebook online here. If you click on any notebook, it will render the notebook. However, you cannot edit the notebook but must download the file to your computer and open it locally.
Getting the codes
You can download all the code using git
git clone https://github.com/cpraveen/na
This will download all the code inside the directory called na. Alternately, you can download a zip file containing all the code here.
You can also read the codes and notebook online here. If you click on any notebook, it will render the notebook. However, you cannot edit but must download the file to your computer and open it locally.
Using notebooks
Python is installed on all the computers in the lab. Open a terminal and type the following to start a notebook
jupyter-notebook
This should start the notebook in your web browser.
Microsoft Azure Notebooks
You can make an account on Microsoft Azure and create a notebook and run in your browser.
On my Azure account, you can see the Python and numerical codes. Note that these are copies of the codes on my github account and may be slightly older, though I try to sync them periodically.