|Course Title||NUMERICAL MATRIX ANALYSIS|
|Full Title||Numerical Matrix Analysis|
|Description||Singular value decomposition. Projections, QR-factorization, orthogonalization, conditioning and stability, Gaussian Elimination, LU-Factorization, pivoting strategies, Cholesky Factorization. Iterative methods for diagonalization and eigensystem computation. Tridiagonal, Hessenberg, and Household matrices. The QR algorithm.|
|Prerequisite||Mathematics 340; and either Mathematics 254, 342A, or Aerospace Engineering 280 with a grade of C (2.0) or better in each course. Proof of completion of prerequisite required: Copy of transcript.|
|General Text||NOTE: Proof of completion of prerequisites required for all upper division courses: Copy of transcript.|
This course will use the Canvas Learning Management System instead of Blackboard as part of an exploratory process this semester. Your professor will provide you more information in the syllabus. Information about the Canvas Pilot can be found at its.sdsu.edu/canvas.