CS 210: Scientific Computing

General

MWF 12:00 PM - 12:50 PM, Student Success Center 308
Discussion A21: Thursday 1:00 PM - 2:50 PM, Chung 143
Piazza: https://piazza.com/ucr/fall2023/cs210/home. (Sign up: https://piazza.com/ucr/fall2023/cs210.)
Textbook: Numerical Algorithms, by Justin Solomon (pdf from author)
Textbook: Scientific Computing, by Michael T. Heath (available through UCR library online)
Syllabus: pdf.

Instructor

Craig Schroeder
Office Hours: MWF 1:00 PM - 2:00 PM (after class), Chung 309, or by appointment
Email: craigs@cs.ucr.edu

Teaching Assistant

Mehrnaz Ayazi
Office Hours: TBD
Email: mayaz003@ucr.edu

Course Summary

This course provides an introduction to key concepts and methods in scientific computing, including numerical linear algebra, solution of linear and non-linear systems of equations, and optimization. The goal is to prepare you to use scientific computing in your area (e.g. graphics, vision, robotics, machine learning, data mining, etc.) or to continue on to further study of special topics in scientific computing.

Schedule

Date	Topic	Reading

09/29	linear algebra review	Solomon 1.1-1.3

10/02	floating-point number representation	Solomon 2.1, Heath 1.2-1.3
10/04	conditioning, stability, accuracy	Solomon 2.2-2.3, Heath 2.3
10/06	linear systems, existence and uniqueness	Solomon 3.1-3.4, Heath 2.1-2.2, 2.4

10/09	LU factorization	Solomon 3.5
10/11	special linear systems	Solomon 4.2, Heath 2.5
10/13	matrix and vector norms	Solomon 4.3

10/16	sensitivity analysis	Solomon 4.3
10/18	orthogonal matrices	Solomon 5.1-5.3, Heath 3.5
10/20	Gram-Schmidt	Solomon 5.4

10/23	Householder transformations	Solomon 5.5-5.6
10/25	midterm review
10/27	midterm

10/30	eigenvalues and eigenvectors	Solomon 6.1-6.2, Heath 4.1
11/01	computing eigenvalues and eigenvectors	Solomon 6.3-6.4, Heath 4.4-4.5
11/03	computing eigenvalues and eigenvectors	Solomon 6.3-6.4, Heath 4.4-4.5

11/06	singular value decomposition	Solomon 7.1-7.2, Heath 4.7
11/08	singular value decomposition	Solomon 7.1-7.2, Heath 4.7
11/10	holiday

11/13	nonlinear equations	Solomon 8.1, Heath 5.1-5.5
11/15	nonlinear equations	Solomon 8.1, Heath 5.1-5.5
11/17	nonlinear systems	Solomon 8.2-8.3, Heath 5.6

11/20	unconstrained optimization	Solomon 9.1-9.3, Heath 6.1-6.2.2
11/22	unconstrained optimization	Solomon 9.4
11/24	holiday

11/27	ordinary differential equations	Solomon 15.1-15.2
11/29	time stepping schemes	Solomon 15.3-15.3.4
12/01	time stepping schemes	Solomon 15.3-15.3.4

12/04	TBD
12/06	final review
12/08	final

Note on academic integrity

All assignments are to be completed individually unless otherwise stated. The following are not allowed in this course. For the purposes of this course, they are violations of academic integrity. Violations of academic integrity will result in a score of 0 for the relevant assignment and a lowering of the final course grade by one letter grade (e.g., from A to B). In more severe or repeat cases, violations will result in an 'F' for the course and a referral to the campus academic integrity committee.

Working on homework with another student or sharing solutions with another student.
Asking or paying anyone to complete any portion of the course for you.
Copying or referring to homework solutions, code, or pseudocode from any source (other than course resources such as the course textbook).
Working on homework in a public Github repository (or anything else that results in your work being visible to other students or visible publicly), whether during or after the course. Working in a private Github repository is permitted, provided that repository stays private forever and is never shared. If you wish to share your code from this course with potential employers, please do so privately.
Looking up answers/hints to homework or coding problems online. ("Researching the question.") The Internet is a very useful resource, and there are many reasonable places for it in this course (python library reference, as a supplement to lectures, further information on interesting topics, etc.) But there is a fine line between using the Internet as a tool for learning and using the Internet as a tool for cheating. If you are not sure, ask.

The following are explicitly allowed.

Office hours (TA or instructor) are a great resource if you are stuck on a problem or otherwise struggling.
This course has a Piazza page; please take advantage of it. If your question would contain code, solutions, or hints, please make the post private.
There are no restrictions on using resources from the course (course textbook, lectures, lecture notes, course website, etc.).

If you find yourself struggling in the course, seek help early. The longer you wait, the fewer options will be available.

Start homework early, especially coding parts. If you start the night before, your chances of successful completion are slim. Although the coding is not intended to take a long time, the time required for debugging is unpredictable and varies wildly from student to student.

Grading

40%	homework
30%	midterm
30%	final