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Matrix computations &
Numerical linear algebra

David Gleich

Purdue University

Fall 2012

Course number CS-51500

Tuesday and Thursday, 10:30-11:45am

Location Lawson 1106


Initial survey -- your course goals

Please answer the following questions in complete sentences in a typed manuscript and submit the solution to me in class on August 23rd or in an email to me and the TA before class.

Date: 2012-08-21

Yourself

0) Please tell me about yourself: name, MS/PhD objective, adviser (if you have one), year in program, research area.

1) Why are you taking the class?

The course

2) Which collaboration policy would you prefer?

3) The homeworks will be a mix of examples, applications, coding, and theory. For instance, I might have a few easy “practice” questions about solving small linear systems. Then I might have a multi-step application that develops a general problem such as… “figure out where people are moving and where they are likely to be in 2050” into a matrix algorithm. There will also be some coding work, such as “write a program to solve a linear system using the LU decomposition without pivoting”. Finally, there will be a theory component to the homeworks. These problems will ask you to prove a matrix statement.

Do you find you learn better with any particular type of problem? If so, which one?

4) Would you be interested in extra credit opportunities that extend the homework questions in more difficult ways? For instance, making your implementations fast or in C++.

5) Would you be interested in sharing any of the matrix problems you encounter with the class in a 3-5 minute presentation?

6) What have other professors done that you’ve found helps you learn?

Numerical computing software

7) Have you used Matlab before?

8) Have you used SciPy before?

9) Have you used R before?

10) Have you used Mathematica before?

11) Any other numerical computing packages?

The course

13) Which of the topics from the syllabus are you most excited about?

14) Anything missing from the syllabus you were hoping to learn about?