CS 4220 / MATH 4260 - Numerical Analysis: Linear and Nonlinear Problems

General Information

Introduction to the fundamentals of numerical linear algebra: direct and iterative methods for linear systems, eigenvalue problems, singular value decomposition. In the second half of the course, the above are used to build iterative methods for nonlinear systems and for multivariate optimization. Strong emphasis is placed on understanding the advantages, disadvantages, and limits of applicability for all the covered techniques. Computer programming is required to test the theoretical concepts throughout the course.

Prerequisites

Math 2940 or any other linear algebra class in the Math department CS 1112 or 1132 (MatLab programming is required in this class)

Topics Covered

• Linear system solve
• Least square approximation
• Eigenvalue problems
• Non-linear system solve
• Optimization
• Non-linear least square approximation
• Sparse Matrices

General Tips

Read the textbook before and after lecture. Van Loan glosses over the proofs behind a lot of the methods in the course. It can be easy to nod your head along and think you understand the lecture because Van Loan is a great lecturer. A solid foundation in linear algebra is crucial. Definitely try to brush up on your lin alg background before taking this class if possible. If you do that, this class will be all the more rewarding.

Workload

7-8 assignments in total, 2 prelims and 1 final

Assignments are relatively short and can be done with a partner

Only 1 midterm in Spring 2014

Why you should take this class

• Take this class if you like linear algebra and consider yourself more hard-core at Math or more quantitative than an average CS major.
• Consider taking this class if you are considering the AI vector
• Take this class because Prof. Van Loan is amazing. His lectures are flawless.

Past Offerings

Resources