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Matrix Theory And Numerical Linear Algebra I

Review of basic linear algebra from a constructive and geometric point of view. Factorizations of Gauss, Cholesky and Gram-Schmidt. Determinants. Linear least squares problems. Rounding error analysis. Stable methods for updating matrix factorizations and for linear programming. Introduction to Hermitian eigenvalue problems and the singular value decomposition via the QR algorithm and the Lanczos process. Method of conjugate gradients.

Prefix:
MA
Course Number:
522
Semester:
Fall 2016
Year:
2017010
Credits:
3.0