Prerequisites: MATH 2331, or equivalent, and six additional hours of 3000—4000 level Mathematics.
Course Description: Conditioning and stability of linear systems, matrix factorizations, direct and iterative methods for solving linear systems, computing eigenvalues and eigenvectors, introduction to linear and nonlinear optimization.
- Introduction to Applied Linear Algebra Vectors, Matrices, and Least Squares, by S. Boyd (Stanford) and L. Vandenberghe (UCLA). ISBN: 978-1316518960.
- Matrix Factorizations, Iterative Methods, Minimization, a class note by L. Vandenberghe (UCLA).
Conditioning and stability of linear systems, matrix factorizations, direct and iter- ative methods for solving linear systems, computing eigenvalues and eigenvectors, introduction to linear and nonlinear optimization.
- Introduction; Vectors
- Norm, distance, angle
- Matrix inverses
- Orthogonal matrices
- QR factorization
- Linear equations
- Least squares
- Least squares applications
- Multi-objective least squares
- Constrained least squares
- Cholesky factorization
- Nonlinear least squares
*Note: Students should visit their instructor's website for Course Structure and Grading Policies, as this information is typically included in the instructor's syllabus. This is a course guideline and is merely an example of what the course structure may be and therefore, should be confirmed with the instructor. Students should contact the instructor for the updated information on current course syllabus, textbooks, and course content
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