MATH/CS 62251/72251: Numerical Analysis I
Meeting times and room: Tu + Th 2:15-3:55 in MSB 158
Instructor: Lothar Reichel
Office: MSB 366
Office hours: Tu + Th 4-5:30 and by appointment
E-mail: firstname.lastname@example.org (Please use this e-mail address!)
The course focuses on Numerical Linear Algebra, which is fundamental for
most areas of Scientific Computing. Many ideas and concept of importance in
applied mathematics and computation will be discussed. These include several
matrix factorization methods, such as QR and LU factorizations, as well as
the singular value decomposition. The sensitivity of the computed results to
errors in the data, as well as to round-off errors introduced during the
computations, will be discussed. It is the purpose of this course to
introduce state-of-the-art numerical methods and provide an understanding
of their performance through analysis and application. The programming
languages MATLAB and GNU Octave will be taught. Octave is similar to MATLAB
and is available for free. The performance and properties of the numerical
methods discussed will be illustrated using MATLAB. All students should get
accounts on the Math/CS Network on which MATLAB is available. GNU Octave is a
public domain language very similar to MATLAB, and can be used for homework
assignments. Instructions on how to install GNU Octave on your PC are available
A very basic MATLAB primer, which helps you get started if you do not know
MATLAB can be found
L. N. Trefethen and D. Bau, ``Numerical Linear Algebra'',
SIAM, Philadelphia, 1997. Contact the instructor before ordering the book.
It might save you money.
Introduction to Numerical Computing I+II or similar
courses. Contact instructor if you would like to take the course, but do
not have any experience in scientific computing.
The desired learning outcomes are described
Vector norms, orthogonal vectors and matrices, orthogonal projections.
Matrix factorizations: QR factorization, the singular value decomposition,
and LU factorization.
Sensitivity to errors: Conditioning and stability.
Eigenvalue and eigenvector computation.
Introduction to iterative methods: the Arnoldi and Lanczos processes.
There will be a mid-term exam and a final exam. The final exam is on Tuesday December 13, 12:45-3:00 p.m.
Homework will be assigned regularly and collected at the end of each major
section. Homework problems can be found
Homework and numerical experiments contribute 33%, the midterm and final
exams contribute 33% each towards your course grade.
The official registration deadline for this course is 9/4/16. University policy requires all students to be
officially registered in each class they are attending. Students who are not officially registered for a course
by the published deadlines should not be attending classes and will not receive credit or a grade for the course.
Each student must confirm enrollment by checking his/her class schedule (using Student Tools in FlashLine) prior
to the deadline indicated. Registration errors must be corrected prior to the deadline.
Attendence requirements are described
, i.e., presenting someone else's work as your own is discussed
This includes finding the answer of a homework problem in a book or in someone
else's assignment, and copying it. Plagiarism, of course, is unacceptable.
Students with disabilities
University policy requires that students with disabilities be provided reasonable accommodations to ensure their
equal access to course content. If you have a documented disability and require accommodations, then please contact
the instructor at the beginning of the semester to make arrangements for necessary classroom adjustments. Please
note, you must first verify your eligibility for these through Student Accessibility Services (contact 330-672-3391
or visit www.kent.edu/sas for more information on registration procedures).