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A continuation of 200-level linear algebra with computational and theoretical aspects and applications.
The course investigates and develops some of the fundamental concepts of matrix algebra, including matrix decompositions, positive definiteness, and solving linear systems. It also explores how matrix algebra can be used in a variety of applications, including problems in commerce, data science, engineering, operations research, and elsewhere.Topics Covered:Topics will be selected from the following list: Vector and matrix norms and condition numbers; QR-factorizations via Givens and Householder matrices; Singular Value Decompositions, Cholesky factorization; Fundamental spaces of a matrix; Least squares, and the use of QR-factorizations and the SVD to find the shortest solutions; Eigenvalues, eigenvectors, and their effcient calculation; Linear programming; Integer programming.
At the end of the course, students will:Be proficient in the standard techniques of matrix algebra;understand why these techniques work;be able to use these techniques in a variety of applications, including using MATLAB to solve problems;have developed problem solving skills both as part of a team and as an individual;have developed written and oral communications skills, emphasizing the ability toexplain what the mathematics means.
One of MATH203, EMTH211, orDATA203
Practical methods of optimization
Noble, Ben. , Daniel, James W;
Applied linear algebra
Linear algebra and its applications
Thomson, Brooks/Cole, 2006.
General information for students
Domestic fee $802.00
International fee $4,563.00
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see
Mathematics and Statistics