CMSC 37810Mathematical Computation 1: Matrix Computation (STAT 30900)
Prerequisites: Linear Algebra (STAT 34300 or equiv.) and some previous experience with Statistics
Long Description: This course starts with a presentation of the fundamental algorithms for the solution of linear equations, the decomposition of matrices, and finite dimensional eigenvalue problems. Applications to least squares/regression will be presented, emphasizing use of existing numerical software. The course will also discuss optimization problems and introduce the basic principles of simulation-based methods. Topics include:
Gaussian elimination and back-substitution; LU decomposition. (General/Symmetric); Singular value decomposition. (Symmetric); Householder orthogonalization and QR factorization (Symmetric); Iterative methods: Jacobi and Gauss Seidel; Optimization: Newton-Raphson and quasi-Newton; Uniform random number generation; Simulating specific distributions; Monte Carlo methods.Instructors: Yali Amit
Quarter offered: AUT
Unverified as of 23 May, 2013.