[ugrads] Applied Math Colloquium today at 4:40pm

Hemanshu Kaul kaul at math.iit.edu
Mon Oct 6 09:17:04 CDT 2008

Dear Students,

I want to encourage you (if you have taken/ are taking Math 350) to attend 
today's colloquium talk by Prof. Fasshauer. This talk will give you a 
flavor of research in computational mathematics thats carried out at IIT 
through an algorithm and its application that should be accesible to most 
senior and junior undergraduate students. This is a good opportunity to 
learn about this exciting field of mathematics.

Over this academic year, we will have similar talks from our faculty 
introducing the research done at IIT in different areas of math. I hope you 
will find these helpful in connecting with research in our department.

Many of you don't seem to be aware of talks held at the department. The 
webpage http://www.iit.edu/csl/am/colloquia/ has the talk schedule of the 
current semester. The details of today's talk are given below as well.

see you at the talk,
Hemanshu Kaul

Hemanshu Kaul
Assistant Professor of Applied Mathematics
Illinois Institute of Technology, Chicago

Solving Ill-Conditioned Symmetric Positive Definite Linear Systems
with Riley's Algorithm
Greg Fasshauer, Illinois Institute of Technology
Monday, Oct. 6,
E1 106    4:40 pm

In practical computing situations one often faces the problem of having to
solve an ill-conditioned system of linear algebraic equations. One of the
best-known approaches to deal with such problems involves the singular
value decomposition which was shown to be computationally feasible by Gene
Golub, William Kahan and Christian Reinsch in the late 1960s and early
1970s. Another technique - perhaps much less known - is due to James Riley
[1]. We will explain Riley's algorithm (which we just recently became
aware of) and compare it to two SVD-implementations on a problem that
arises when determining optimal shape parameters for radial basis function
interpolants of scattered data.
This talk is based on joint work with Paritosh Mokhasi (MMAE, IIT) and
Jack Zhang (University of New Mexico).

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