Math 4064 - Numerical Linear Algebra

Syllabus

Instructor: Xiaoliang Wan

Lecture: TTH 1:30-2:50pm, 134 Lockett Hall

Office Hours: TTH 11:30am-1:00pm

Text: Lecture Notes on Numerical Linear Algebra by S. C. Brenner and L.-Y. Sung

Course Description: This is an introductory course on numerical linear algebra. Topics include Gaussian elimination and iterative methods for linear systems, vector and matrix norms, condition numbers, power methods for eigenvalue problems, and least square problems.

Grade: Your final grade will be based on your performance on projects and exams: Quizzes (30%), Homework (30%), Midterms (20%), and Final exam (20%). There is a weekly quiz at the beginning of each Thursday class. The two lowest scores will be dropped and there will be no make-up for quizzes.

Programming: Python or MATLAB is recommended for the programming projects. The MATLAB Guide (2nd Edition) by D. J. Higham and N. J. Higham (SIAM, ISBN:0-89871-578-4) is recommended as a reference for Matlab. The two python libraries that are closely related to this course are Numpy (https://numpy.org/doc) and Scipy (https://docs.scipy.org/doc).

Instructor:	Xiaoliang Wan
Lecture:	TTH 1:30-2:50pm, 134 Lockett Hall
Office Hours:	TTH 11:30am-1:00pm
Text:	Lecture Notes on Numerical Linear Algebra by S. C. Brenner and L.-Y. Sung
Course Description:	This is an introductory course on numerical linear algebra. Topics include Gaussian elimination and iterative methods for linear systems, vector and matrix norms, condition numbers, power methods for eigenvalue problems, and least square problems.
Grade:	Your final grade will be based on your performance on projects and exams: Quizzes (30%), Homework (30%), Midterms (20%), and Final exam (20%). There is a weekly quiz at the beginning of each Thursday class. The two lowest scores will be dropped and there will be no make-up for quizzes.
Programming:	Python or MATLAB is recommended for the programming projects. The MATLAB Guide (2nd Edition) by D. J. Higham and N. J. Higham (SIAM, ISBN:0-89871-578-4) is recommended as a reference for Matlab. The two python libraries that are closely related to this course are Numpy (https://numpy.org/doc) and Scipy (https://docs.scipy.org/doc).