Math 430/630 - Matrix Analysis
Fall 2002 - Matthias K. Gobbert
Section 0101 - Schedule Numbers 3699/3730
This page can be reached via my homepage at
Final scores and grades ordered by your assigned number:
- Matthias K. Gobbert,
Math/Psyc 416, (410) 455-2404, firstname.lastname@example.org,
office hours: TTh 04:00-05:00 or by appointment
- Lectures: TTh 05:30-06:45, MP 401;
see the schedule for more information.
- Prerequisites: Math 251, Math 301, CMSC 201,
or instructor approval
- Copies of all following books are on reserve in the library.
Lloyd N. Trefethen and David Bau, III,
Numerical Linear Algebra,
SIAM, 1997 and 2000.
Notice that the bookstore may stock the book under Math 430.
Note: SIAM has published both a softcover (1997) and a hardcover (2000)
version of this book; they are identical in content, and you may use
either one of them.
Recommended book on Iterative Methods:
David S. Watkins,
Fundamentals of Matrix Computations,
second edition, Wiley, 2002.
Recommended book on Matlab:
Desmond J. Higham and Nicholas J. Higham,
Matlab Guide, SIAM, 2000.
Webpage of the book
including list of errors.
- Grading policy:
The homework is weighted so heavily, because it includes the
computer assignments that are vital to understanding the course material.
The presentations consist of presenting selected homework problems
in class on the board; I will assign the problems to individual students
throughout the semester.
Both Math 430 and 630 will have projects with professional grade type-set
reports, but they will be different in level;
they will be assigned as early as possible and are due
before the end of classes.
See also the general policies and procedures for more information.
Matrix Analysis encompasses the theory of matrices as well as the
practice of using numerical methods to implement the associated algorithms
in a computer. The most classical example of a computational technique
that is used both for hand calculations as well as in the computer
is Gaussian elimination to find the solution to a linear system of equations;
a version of this algorithm is known as reduction to row echelon form.
Starting with knowledge from basic linear algebra, we will build up
familiarity with advanced concepts and their application.
The course will start by introducing basic definitions like vector and
matrix norms and the singular value decomposition.
In addition to linear system of equations, we will study least-squares
problems and eigenvalue computations, and various numerical methods to
solve them. Those methods include both
direct methods (that produce the solution in a fixed number of steps)
and iterative methods (that get closer to the solution the more steps
are taken). We will discuss advantages and drawbacks of both types
of methods, based both on theoretical considerations and implementation
Information for Download
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- setupA.m Matlab function for setup of system matrix
Official UMBC Honors Code
By enrolling in this course, each student assumes the responsibilities of
an active participant in UMBC's scholarly community in which everyone's
academic work and behavior are held to the highest standards of honesty.
Cheating, fabrication, plagiarism, and helping others to commit these acts
are all forms of academic dishonesty, and they are wrong. Academic
misconduct could result in disciplinary action that may include, but is
not limited to, suspension or dismissal.
To read the full Student Academic Conduct Policy, consult the
UMBC Student Handbook, the Faculty Handbook, or the UMBC Policies
section of the UMBC Directory.
Copyright © 1999-2002 by Matthias K. Gobbert. All Rights Reserved.
This page version 2.5, December 2002.