APPM 3310 Matrix Methods

Fall 2011

Section Instructor Office Office Hours
001 Sujeet Bhat
ECOT 338
MWF 10-11, MW 2-3

002 Christopher W. Curtis ECCR 251 T 10-1

Section	Instructor	Office	Office Hours
001	Sujeet Bhat	ECOT 338	MWF 10-11, MW 2-3
002	Christopher W. Curtis	ECCR 251	T 10-1

Learning Assistants: Chris, Eric, Paul, Thomas

LA Sessions

Lecture 001 is on MWF 9-9:50 a.m. in ECCR 151.
Lecture 002 is on MWF 2-2:50 p.m. in ECCR 151.
Syllabus (PDF)
Class Calendar (PDF)
Homework problems and solutions : Please see the class calendar for the homework schedule. Late homework will NOT be accepted, especially after the solutions have been posted!
Exam Solutions
Semester Project (PDF) info is here!
Text: Applied Linear Algebra, by Peter J. Olver and Chehrzad Shakiban (1st Edition, 1st or 2nd printing)
Corrections to the first printing (PDF)
Exams
The exams will be closed book and no calculators or other electronic devices are permitted.
1. Exam I: Friday, September 30, in class
2. Exam II: Friday, November 4, in class.
3. The Final Exam is cumulative. The final for Lecture 001 is Wednesday, Dec 14th, 7:30 - 10 p.m. and the final for Lecture 002 is Monday, December 12th, 1:30 p.m. - 4 p.m. .
4. Here are some exams from previous semesters. Please note that not all of the first exams correspond to first exam material for our class. In other semesters, different texts may have been used and the material may have been covered at a different pace.

Matlab Examples
1. Matlab Basics
2. GaussElim.m, a simple program to do Gaussian Elimination on a regular matrix
3. PGaussElim.m, Gaussian Elimination with permutation for a nonsingular matrix.
4. Face Data is data you can use to try out the solftware below.
5. Principal Components is code you can use to calculate the principal components of 2D data. Try it on the data given above.
6. Gaussian and Confidence are routines that calculate the confidence in a measurement, relative to given data.
7. Eigshow is a Matlab demo of the key idea behind the SVD.
8. This gives more information about the SVD, including applications.
A few years ago, some people (D. Bundy, E. Gibney, J. McColl, M. Mohlenkamp, K. Sandberg, B. Silverstein, P. Staab, and M. Tearle) in Applied Math developed a method to gently teach mathematical writing. Martin Mohlenkamp maintains the Good Problems site that this effort produced. This is an excellent site for guidelines on how to write mathematics well.