MATH 603-01, Class Number 4988, Fall 2019
Matrix Analysis

Course information

Course: MATH 603 Matrix Analysis
Time/Place: TuTh 4:00PM - 5:15PM, SOND 202
Instructor: Dr.  Jacob Kogan Teaching Assistant:
Office: MP 427
Phone: 410-455-3297
Email: kogan at math.umbc.edu
Office hours: TuTh 6:45 PM-7:30 PM and by appointment

Pi Mu Epsilon: The Buddy System

Textbook: Matrix Analysis and Applied Linear Algebra, by Carl Meyer, SIAM, 2000.

(you may want to check chegg, or fetchbook, or vew the book, or download.)
Some Other Relevant Books: Prerequisites: Math 221, Math 251 and Math 301 or permission of instructor. You will be expected to do proofs and we will discuss proofs a lot in class.

Your Review: I will assume that you are familiar with the following sections from Meyer's book: 1.2, 1.3, 2.1-2.5, 3.2-3.6 and you can handle related problems. The material is basically the same as that covered in Sections 1.1-1.8, 2.1-2.3 of Lay's book (the Math 221 text). We will use this review material extensively in our course, but I will not discuss it in class. I will assign homework on this material.

Material Covered: In class we will cover the following sections/chapters of Meyer's book: Sections 3.7-3.10, 4.1-4.8, 5.1-5.15, Chapter 6, Sections 7.1-7.8, 8.2-8.4. Some sections may be omitted.

Grading

Grades: Quiz 20 pt, two Midterms 40 pt each, Final 100pt, Project 20 pt.
Homework: The homework problems will be posted on the course web page for each week of class. You may ask me questions about the homework and you may collaborate with another student in the class and you are encouraged to do so. However quizzes and exams write up is your own [two (almost) identical solutions may both be given zero]. I do not encourage large groups of people to work together on homework. Do not miss class to complete a homework.

Project: (an opportunity to earn additional 20 pt): The project is based on one of the articles

  1. The $25,000,000,000 Eigenvector: The Linear Algebra Behind Google by Kurt Bryan and Tanya Leise, SIAM review, Vol. 48, Num. 3, Sept. 2006, pp. 567-581
  2. A Survey on PageRank Computing by Pavel Berkhin, Internet Mathematics, Vol. 2, No. 1, 2005, pp. 73-120
  3. Principal Direction Divisive Partitioning by Daniel Boley, Data Mining and Knowledge Discovery, 2(4):325 - 344, Dec. 1998.
  4. Scalable Data-driven PageRank by Joyce Jiyoung Whang, Andrew Lenharth, Inderjit S. Dhillon, and Keshav Pingali, International European Conference on Parallel and Distributed Computing(Euro-Par), pages 438-450, 2015.
  5. Matrix Completion with Noisy Side Information by K.-Y. Chiang, C.-J. Hsieh, and I. S. Dhillon, Proceedings of the Neural Information Processing Systems Conference(NIPS), pages 3447-3455, 2015.
  6. Matrix Completion by Emmanuel J. Candes and Benjamin Recht
The project is due Tuesday, November 19, 2019 at the start of class. In your project write up you summarize the main points of the paper. You can work in a group of two. Both students are expected to contribute equally to the work. Submit ONE project write up, both are expected to present the paper. If you elect to work on the project with a partner, then you must send me email stating who you are working with by Tusday, October 8, 2019. You are encouraged to start working immediately. The project only relies on an understanding of Math 221 material. You should start it now! (teams)
Quizzes and Exams: There will be one quiz, two midterms, and the final. The tentative dates are below: The exact dates and material covered will be announced in class at least a week before the exam.
There will be no make up quizzes and exams.

Final Exam: Tuesday, December 17, 2019; 3:30 pm-5:30 pm.

Letter grade cutoffs are expected to be the following:
Percentage ≥ 90% 89% ≥ and ≥ 80% 79% ≥ and ≥ 70% 69% ≥ and ≥ 60% 59% ≥
Letter Grade A B C D F

Remember: Mathematics is NOT a spectator sport.
Read through the relevant section of the text before each lecture.
extra credit problems and submission form (if you need to convert your file to pdf you may consider using zamzar), and solutions.
homework problems
old comps

The Official UMBC Honors Code

By enrolling in this course, each student assumes the responsibilities of an active participant in UMBC's scholarly community in which everyone's academic work and behavior are held to the highest standards of honesty. Cheating, fabrication, plagiarism, and helping others to commit these acts are all forms of academic dishonesty, and they are wrong. Academic misconduct could result in disciplinary action that may include, but is not limited to, suspension or dismissal.

To read the full Student Academic Conduct Policy, consult the UMBC Student Handbook, the Faculty Handbook, or the UMBC Policies section of the UMBC Directory.