Linear algebra from a numerical solution perspective. Topics include direct and iterative methods for solving linear systems; vector and matrix norms; condition numbers; least squares problems; orthogonalization, singular value decomposition; computation of eigenvalues and eigenvectors; conjugate gradient methods; preconditioners for linear systems; computational cost of algorithms. Topics will be supplemented with programming assignments. Matrix factorizations, conditioning and stability, Krylov subspace methods and reconditioning.
Prerequisite: Graduate Standing (Undergraduate Analysis and Linear Algebra)
📊 Grading Policy
[ Office hours](https://peridot-wash-2b0.notion.site/Office-hours-750bec9f79184ec196d828f486c8ffeb)
📜 Mid Exam(20%)
📜 Final Exam(30%)