Instructor: Ari Stern

Email: astern@math.wustl.edu

Office: Cupples I, 211B

Office Hours: TuTh 1-2pm

Problem sets will be posted approximately biweekly, and will be
collected at the beginning of class on the due date. You are
encouraged to discuss the homework with your fellow students and
to collaborate on problems, but
*your final write-up must be your own*. Please make sure that your
solutions are written clearly and legibly.

Jeff Zhang (jeffzhang@wustl.edu) is responsible for grading the homework assignments.

- HW1: handout [pdf], code [py]. Due Wednesday, September 2 Friday, September 4.
- HW2: handout [pdf], code [py]. Due Friday, September 18.
- HW3: handout [pdf]. Due Friday, September 25.
- HW4: handout [pdf], code [py]. Due Friday, October 9.
- HW5: handout [pdf], code [py]. Due Wednesday, October 28.
- HW6: handout [pdf]. Due Friday, November 13.
- HW7: handout [pdf]. Due Monday, November 23.
- HW8: handout [pdf]. Due Friday, December 4.

Lectures will be held MWF 3-4pm, in Mallinckrodt 305. The first class will be on Monday, August 24, and the last will be on Friday, December 4. Class will be canceled for Labor Day (Monday, September 7), Fall Break (Friday, October 16), and Thanksgiving Break (Wednesday, November 25, and Friday, November 27).

There was one in-class midterm exam, held on Wednesday, October 14. The final exam was held on Thursday, December 10, from 6-8pm.

Grades will be based on a weighted average of homework (40%, lowest score dropped), midterm exam (20%), and final exam (40%).

The text for this course is *An Introduction to Numerical
Analysis*, by Endre Süli and David Mayers,
published by Cambridge University Press. (Note: The Amazon
Kindle eBook version of this text is *not*
recommended, since the Kindle software does not always
display mathematical formulas properly.)

The programming component of this class is based on the
Python programming language
with the SciPy collection of
numerical and scientific computing tools. No previous experience
with either is assumed (although experience with *some*
programming language is a prerequisite). This software is free
and open source, and can be installed on your own computer.

The Anaconda Python Distribution is officially recommended for this course, and is available for Linux, Mac, and Windows.

Computer arithmetic, error propagation, condition number and
stability; mathematical modeling, approximation and convergence;
roots of functions; calculus of finite differences; implicit and
explicit methods for initial and boundary value problems;
numerical integration; numerical solution of linear systems,
matrix equations, and eigensystems; Fourier transforms;
optimization. Various software packages may be introduced and
used. *Prerequisites*: CSE 131 or 200 (or other computer
background with permission of the instructor); Math 217 and
309.