Numerical and Scientific Computing

This is the course page for Numerical and Scientific Computing (CSL361), for Semester I, 2009-2010, being taught by Subhashis Banerjee at the Department of Computer Science and Engineering, IIT, New Delhi.

General Information | Notices | Tutorials | Assignments | Examinations | Resources (available only from within IITD)|

General Information

Notices

• Check out the video lectures on linear algebra by Gilbert Strang.
• Check out Abhiram Ranade's tutorial on Some uses of spectral methods in Computer Science (applications of SVD).
• Check out a tutorial on conjugate gradient method in the Resources directory.

• Minor II (Take-home part):   PDF, html.

1. Problem set #1: Numerical computations - pitfalls. PDF, html.
2. Problem set #2: To err is human and to blame it on a computer is more like it. PDF, html.
   To be done as home-work. This problem set will not be discussed in the class. See What Every Computer Scientist Should Know About Floating-Point Arithmetic for background reading.
3. Problem set #3: Polynomials and interpolation PDF, html.
4. Problem set #4: Some basic Linear Algebra. PDF, html.
5. Problem set #5: Speeding up computations is an art and block matrix operations help. PDF, html.
6. Problem set #6: Error analysis of $LU$ and some extensions. PDF, html.
7. Problem set #7: SVD, Orthogonal projections, Least squares etc. PDF, html.
8. Problem set #8: Householder and Givens QR. PDF, html.
9. Problem set #9: Eigenvalue computation. PDF, html.
10. Problem set #10: Steepest descent, Conjugate gradient etc. PDF, html.

1. Do the numerical computations of Problem set #1 (above) in MATLAB.
2. Gaussian elimination. PDF, html. Click here to submit.
3. SVD applications. PDF, html. Last date for submission to be announced.
4. Assignment #4: SVD, Eigenvalues etc. PDF, html.