Instructor: Ari Stern

Email: astern@math.wustl.edu

Office: Cupples I, 211B

Office Hours: TuTh 10:30am-12pm

Problem sets will be posted approximately weekly, 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.

Melissa Zhang (melissa.zhang@wustl.edu) is responsible for grading the homework assignments.

- HW1: handout [pdf], code [py]. Due Wednesday, September 3.
- HW2: handout [pdf], code [py]. Due Friday, September 12.
- HW3: handout [pdf]. Due Wednesday, September 24.
- HW4: handout [pdf], code [py]. Due Friday, October 3.
- HW5: handout [pdf], code [py]. Due Wednesday, October 15.
- HW6: handout [pdf]. Due Wednesday, November 5.
- HW7: handout [pdf]. Due Friday, November 14.
- HW8: handout [pdf]. Due Wednesday, December 3.

Lectures will be held MWF 3-4pm, in Cupples II, L015. The first class will be on Monday, August 25, and the last will be on Friday, December 5. Class will be canceled for Labor Day (Monday, September 1), Fall Break (Friday, October 17), and Thanksgiving Break (Wednesday, November 26, and Friday, November 28).

The midterm exam was held in class on Wednesday, October 22. The final exam is scheduled for Thursday, December 11, 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.