|Course catalog entry||3:30-4:45pm MW||Digital Computer Laboratory|
|Prof. Paul Fischerfirstname.lastname@example.org||Thursday 9:30 - 11:00||4320 Siebel Center|
|Josh Bevanemail@example.com||Monday 12:00 - 2:00||SC 0207|
|Nick Christensenfirstname.lastname@example.org||Wednesday 1:00 - 3:00||SC 3405|
|Setare Hajarolasvadiemail@example.com||Monday 9:00 - 11:00||SC 0207|
|Thilina Rathnayakefirstname.lastname@example.org||Tuesday 1:00 - 3:00||SC 0207|
Scientific Computing: An Introductory Survey by Michael T. Heath, McGraw-Hill, 2nd edition, 2002
Recorded lectures will be posted on the Echo360 Site. To access Echo360, follow these instructions. Once you access the Echo 360 site, you should see CS450 listed in your dashboard. The recorded lectures can be found under this listing.
Exams: two midterms and a final exam, both of which will be offered in the Computer-Based Testing Facility (located in room 57 Grainger Library).
Quizzes: due before most classes, to be taken online on this website.
Homework: assignments due every two weeks (see detailed schedule below). Homework will involve both written exercises and computer problems. The latter must be done in Python.
Projects: students taking the course for 4 credit hours must complete a term project. See detailed schedule below for various due dates. More project details can be found at the bottom of this page.
More detailed policies on exams, quizzes, and homework are given below.
|date||topic||homework||quiz||project (4-credit only)|
|M Aug 28||Scientific computing|
|W Aug 30||Linear systems||Quiz 1 due|
|M Sep 4||Labor Day|
|W Sep 6||Linear systems||Quiz 2 due|
|M Sep 11||Linear systems||Quiz 3 due|
|W Sep 13||Linear systems||Quiz 4 due|
|F Sep 15||HW 1 due|
|M Sep 18||Linear least squares||Quiz 5 due|
|W Sep 20||Linear least squares||Quiz 6 due|
|M Sep 25||Eigenvalue problems||Quiz 7 due|
|W Sep 27||Eigenvalue problems||Quiz 8 due|
|F Sep 29||HW 2 due|
|M Oct 2||Eigenvalue problems||Quiz 9 due|
|W Oct 4||Nonlinear equations||Quiz 10 due|
|M Oct 9||Nonlinear equations, Secant method|
|T Oct 10||Quiz 11 due|
|W Oct 11||Optimization||Quiz 12 due|
|M Oct 16||Optimization||Quiz 13 due|
|W Oct 18||Optimization||Quiz 14 due||Proposal due|
|U Oct 19||HW 3 due|
|M Oct 23||Interpolation||Quiz 15 due|
|W Oct 25||Interpolation||Quiz 16 due|
|M Oct 30||Numerical quadrature||Quiz 17 due|
|W Nov 1||Numerical quadrature||Quiz 18 due|
|F Nov 3||HW 4 due|
|M Nov 6||Numerical quadrature||Quiz 19 due|
|W Nov 8||IVPs for ODEs||Quiz 20 due|
|M Nov 13||IVPs for ODEs|
|T Nov 14||Quiz 21 due|
|W Nov 15||IVPs for ODEs|
|R Nov 16||Quiz 22 due|
|M Nov 20||Fall break|
|W Nov 22||Fall break||Progress report due|
|M Nov 27||BVPs for ODEs||HW 5 due|
|T Nov 28||Quiz 23 due|
|W Nov 29||BVPs for ODEs|
|TH Nov 30||Quiz 24 due|
|M Dec 4||PDEs||Quiz 25 due|
|W Dec 6||PDEs|
|TH Dec 7||Quiz 26 due|
|M Dec 11||PDEs|
|W Dec 13||No class||Quiz 27 due||Final report due|
|U Dec 14||HW 6 due|
|Course Grade||Total Score as weighted above|
All students taking the course for 4 credit hours must complete a project. This project is chosen by the student with the consent of the instructor.
To ensure that a given project is appropriate in scope and content, students must submit a brief description of the proposed project for approval before beginning implementation. The instructor may suggest modifications or alternatives, if appropriate. Projects are evaluated for both correctness and creativity. Projects are graded as satisfactory/unsatisfactory and make no contribution to the final grade, only to the hours of credit received.
Progress proposals, reports and final reports will be submitted on Relate (the course website). Additional details regarding expectations for the progress report and the final project report will be given in the individual submission spots. Due dates for each portion are shown in the schedule above.
Possible types of projects include:
If you are unsure about what project to propose or what sort of project is acceptable, please consult with the instructor.
Python is the required programming language for completing the homework problems for this course. Listed below are many good resources available for Python, Numpy/Scipy, and Matplotlib.