APPM 3310 Matrix Methods

Summer 2011

Section 002
Instructor: Sujeet Bhat
Office: ECOT 338
Email: sujeet.bhat@colorado.edu
Office Hours: MTWR 11:30am - 2:30pm

Section 002
Instructor: Sujeet Bhat Office: ECOT 338 Email: `sujeet.bhat@colorado.edu` Office Hours: MTWR 11:30am - 2:30pm

Class is on MTWR 9:15-10:50 a.m. in ECST 1B21.
Syllabus (PDF)
Class Calendar (PDF)
Homework problems and solutions : The homework that you turn in will be graded. It is your responsibility to work through as many problems as required for you to master the material.
Exam Solutions
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: Thursday, June 16, in class The exam will cover the material in Chapters 1 and 2, including homework and examples from lecture.
2. Exam II: Thursday, July 7, in class. The exam will cover the material in Ch. 2-Ch. 5.
3. The Final Exam is cumulative. The Final is Thursday, July 21, in class .
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.