MATH 170A Syllabus

General

Textbook: Fundamentals of Matrix Computations, Third Edition by Watkins. Homework problems will be assigned from this book.

MATLAB: MATLAB (from "matrix laboratory") is a programming language and numerical computing environment often used in applied mathematics and other applications. Many assignments (and even test questions) will be to write short programs for MATLAB.

An intro textbook discussing Matlab can be found here.

There are four main ways to get access to MATLAB:

UCSD students can download it for free.
In the basement of AP&M there are computer labs with Matlab installed on all the computers.
You can use the UCSD virtual computer lab (from home or anywhere). Log in with your UCSD credentials.
You can buy a student copy of the software at the bookstore for $ 100.

Alternatively, you can download the free open source version called Octave. While Octave and MATLAB are designed to be compatible, there are differences, and Octave is at least marginally slower. If you join a project/company that uses one of these tools, you will need to use the one they use. More places use MATLAB than Octave. However, Octave is free, as part of the GNU project, which can be an advantage.

We will do some basic MATLAB programming in this course. While we will talk about the MATLAB specific programming details during class, I will expect that you know some programming basics, including what a "for loop" is. (The for loop is about the most complicated programming concept we'll use, but fortunately it's not too complicated.) If you are not comfortable with what a for loop is, or want a short review of basic programming in MATLAB, a good resource is this file.

Course outcomes

For this course, the main subject material is

Solving Ax=b type problems (triangular systems, Cholesky decomposition, banded/sparse systems, Gaussian elimination with and without pivoting, LU decomposition, QR decomposition, iterative methods [steepest descent, conjugate- gradient, etc.])
Understanding the theory behind computation (rounding errors, sensitivity, condition numbers, backward error analysis, backward stability)
Least squares problem (Gram-Schmidt, orthogonal matrices, QR decomposition)
Basic iterative solving for eigenvalues (power method and simple extensions)
Singular value decomposition

By the end of the course, you should be able to show understanding and mastery of the subject material

by performing calculations, including knowing which tools to use in which circumstances by writing MATLAB programs to perform these calculations
by clearly explaining concepts, processes, definitions and theorems
by proving some results related to the material

Homework

Homework will be due Friday of each week by the end of day. Late homework will not be accepted, however, one homework will be dropped when determining the final grade for the class. This should be reserved for emergencies.
Homework dropping is implemented as follows: We have a total of 8 homeworks this quarter (homework 0 to homework 7). The maximal amount of points available is 220. A total of 190 points will be used as 100% for computing the homework part of your grade. This means that if you have a total of 190 points (or more), you will receive 100% on the homework.

All standard homework assignments will be turned in via Gradescope.com. Your login is your UCSD email.

All students enrolled in MATH170A lecture B have been added to Gradescope. If you have not been added (due to late enrollment, for example), please add yourself with the Gradescope entry code 9V6J6Z and use your UCSD email.

Assignments should be uploaded in a single pdf file, or as a picture for each question. Please make sure your files are legible before submitting.

All grading, including the midterm and final, will be done on Gradescope. Regrade requests must be sent via Gradescope as well.

Exams

There will be two midterms and one final for this class.
For all exams: Bring pen/pencil and your students ID.
For all exams: NO calculators, electronic devices, books, or notes allowed.

Exam	Date	Time	Room	Material
Midterm 1	M Jan 27	in class	in class	Everything until Jan 24 lecture/homework
Midterm 2	M Feb 24	in class	in class	Focus on Jan 29 - Feb 21 lecture/homework
Final	W Mar 18	3 - 6 pm	online via zoom	Everything (except 5.6) discussed in lecture/homework

Sample exams:

Sample questions for Midterm 1
Sample questions for Midterm 2
Sample questions for Final

Other sample exams (from Fall 2018): Midterm 1, Midterm 2, Final. Note that these are just sample exams (and provided as is), the exams for our lecture could be very different. Also, in Fall 2018, material was covered in a different order compared to our lecture.

Grading policy

Your grade will be based on the scores of the homework, two midterms, and one final exam. It will be calculated from taking the maximum of the methods defined below:

Method #1 = (20% HW) + (20% Midterm) + (20% Midterm) + (40% Final)

Method #2 = (20% HW) + (25% Best Midterm) + (55% Final)

Method #3 = (20% HW) + (40% Midterm 1) + (40% Midterm 2)

There will be no make-up exams. If you miss a midterm, your grade will be computed using method 2. If you miss the final, your grade will be computed using method 3. We use the standard grading scale

A+	A	A-	B+	B	B-	C+	C	C-	D	F
[96,100]	[92,96)	[90,92)	[86,90)	[82,86)	[80,82)	[76,80)	[72,76)	[70,72)	[60,70)	[0,60)

You are guaranteed this scale, that means that your grade will not be worse than specified by the above scale. There might be an adjustment (=improvement) of grades based on the overall class performance.

Academic Integrity: Academic integrity is highly valued at UCSD and academic dishonesty is considered a serious offense. Students involved in an academic integrity violation will face an administrative sanction which may include suspension or, in very serious cases, expulsion from the university. Your integrity has great value: Cultivate and protect your academic integrity. For more about academic integrity and its value, visit the UCSD Academic Integrity Website.