AMS 526: Numerical Analysis I (Numerical Linear Algebra)
Fall 2009
Time: Monday & Friday 12:50 pm - 2:10 pm Location: Melville Library N3074



Instructor: Prof. Xiangmin (Jim) Jiao
Email: xjiao@sunysb.edu Phone: 631-632-4408
Office Hours: Wed. & Thur 2:00pm-3:00pm, or by appointment
Office: Math Tower 1-115
TA/Grader: Mr. Vladimir Dyedov
Email: vladimir@ams.sunysb.edu
Office Hours: Tue. & Thur. 3:00pm-4:00pm or by appointment
Office: Math Tower S-250



[ Announcements | Course Description | Course Outline | Course Policy | Homework and Tests | Class Schedule  | Links ]

Announcements (back to top)
 
  • 09/30: You can log into BlackBoard to check the grades for your homework.

  • 09/30: Sample questions and sample solutions for Test 1 have been posted.

  • 09/20: Homework 2 has been posted.

  • 09/09: The Mathlab is now open. You need to go to Mathlab during its open hours to activate your account on the Linux computers for doing programming assignments. The schedule for the Mathlab is posted at http://moya.ic.sunysb.edu/Sinc/Remotes/Math/. Note that after you activate your account, you can remotely ssh to compute.mathlab.sunysb.edu at any time using your own or other computer.
Course Description (back to top)


Direct methods for solving simultaneous linear equations. Matrix factorization, conditioning, stability, and efficiency. Computation of eigenvalues and eigenvectors. Singular value decomposition.

Prerequisite/Co-requisite: AMS 505, AMS 595 (co-requisite for students without programming experience in C).

Required Textbook

  • Numerical Linear Algebra, by Lloyd N. Trefethen and David Bau, III, SIAM, 1997, ISBN 0-89871-361-7.

Reference Book (not required)

  • Matrix Computations, 3rd edition, by Gene H. Golub and Charles F. Van Loan, John Hopkins University Press, 1996, ISBN 0-8018-5414-8.
Course Outline (back to top)


Outline

  • Matrix fundamentals, orthogonality, norms, and SVD (2.5 weeks).
  • QR factorization, projectors, Gram-Schmidt algorithm, Householder triangulation, least squares problems (2 weeks).
  • Conditioning and stability (2.5 weeks).
  • Solution of linear system of equations, Gaussian elimination, pivoting, Cholesky factorization (2 weeks).
  • Eigenvalue problems, Hessenberg tridiagonalization, Rayleigh quotient, inverse power method, QR algorithm, Computing SVD (3 weeks).
  • Overview of iterative methods; Arnoldi/Lanczos iteration (1.5 week).
  • Linear algebra software (1 week).


Course Policy (back to top)


Assignments

Homework assignments are due in class typically one week after they are assigned. You are allowed to discuss course materials and homework problems in small groups, but limited to discussion of general ideas only. You must write your solutions completely independently. Under no circumstances may you copy solutions from any source, including but not limited to other students solutions, official solutions distributed in past terms, and solutions from courses taught at other universities. Violation of these rules may result in disciplinary actions.

Exams

The exams (including two tests and the final exam) are closed-book, but you are allowed to bring a single-sided, one-page, letter-size cheat sheet, which you must prepare by yourself.

Attendance

All students are expected to attend all the lectures and exams.

Grading

  • Assignments: 30%
  • Two tests: 40%
  • Final exam: 30%

Note: The lowest score of your homework assignments will be dropped.

 

Homework and Sample Tests (back to top)


For the computing assignments, you are encouraged to use the Mathlab SINC Site at Math Tower S-235. You can remotely log onto the Linux computer  compute.mathlab.sunysb.edu using ssh. Before you can login, you may need to go to Math Tower S-235 to activate your account. You may use your own computer if it runs a UNIX system (such as Linux or Mac OS X), has a C compiler (such as gcc) and debugger (such as gdb and ddd), and has octave,  gnuplot, and gv (for plotting).


NOTE: The solutions are password protected. The username is ams526, and the password is fall2009.

Assignments

Sample Tests
Class Schedule (back to top)
 
  • Important: All schedules are tentative and are subject to change.

Week Date Topic Slides Reading Notes
1 Mon 08/31 Course overview; matrix-vector multiplication slides
§I.1

Fri 9/4 Orthogonal vectors and matrices slides
§I.2 HW1 out
2 Mon 9/7 No class (Labor Day observed)




Fri 9/11 Orthogonal matrices; vector norms slides §I.3
3 Mon 9/14 Matrix norms slides §I.3  

Fri 9/18 Singular value decomposition slides
§I.4-5 HW1 due; HW2 out
4 Mon 9/21 Projectors, QR factorization slides
§II.6-7

Fri 9/25 Gram-Schmidt orthogonalization slides §II.8
5 Tue 9/29 Householder triangularization and Givens rotation slides §II.10
 

Fri 10/2 Least squares problems; review
slides §II.11 HW2 due; HW3 out
6 Mon 10/5 Test 1 (covers Part I and Part II of text book)




Fri 10/9 Floating-point arithmetic; condition numbers slides §III.12-13
 
7 Mon 10/12 Discussion of Test 1; accuracy and stability slides
§III.14
 

Fri 10/16 Stability of algorithms slides §III.15  
8 Mon 10/19 Stability of Householder QR and back substitution
slides §III.16-17  Octave demo trace

Fri 10/23 Conditioning of least squares problems
slides §III.18-19 HW3 due; HW4 out
9 Mon 10/26 Gaussian elimination and LU factorization slides §IV.20-21  

Fri 10/30 Stability of LU slides §IV.21-22  
10 Mon 11/2 Cholesky factorization; linear algebra software slides §IV.23

Fri 11/6 Eigenvalue problems; review
slides §V.24 HW4 due; HW5 out
11 Mon 11/9 Test 2 (covers material up to 11/6)    

Fri 11/13 Reduction to Hessenberg form slides §V.25-26  
12 Mon 11/16 Rayleigh Quotient Iteration slides §V.27  

Fri 11/20 QR algorithm without shifts; QR algorithm with shifts slides §V.28-29 HW5 written-part due; HW6 out
13 Mon 11/23 Other eigenvalue algorithms
§V.30

Fri 11/27 No class (Thanksgiving break)      
14 Mon 11/30 Computing SVD

§V.31 HW5 programming part due

Fri 12/4 Overview of iterative methods; Arnoldi iterations
§VI.32,33  
15 Mon 12/7 Lanczos iterations
§VI.33,36

Fri 12/11 Review


HW6 due
16 Wed 12/16 Final exam (2:15-4:45pm, Melville Library N3074)      

Links (back to top)
 
  • Netlib contains a collection mathematical software. Some particularly useful software for numerical linear algebra include BLAS, LAPACK, ScaLAPACK, etc.