Lecture schedule

This schedule is subject to change, but will be kept up-to-date:

Lecture	Date	Topic	Prior reading	Supplemental videos
L1	1/6/25	Course overview and proof by induction	AG 1.1-1.2
L2	1/8/25	Linear algebra overview	AG 4.1 & 4.3
L3	1/10/25	Vector norms	AG 4.2	here
L4	1/13/25	Matrix norms	AG 4.2	here
L5	1/15/25	Floating point and conditioning	AG 2.2
L6	1/17/25	Stability and accuracy	AG 1.3 & AG 5.8; How to get Meaningless Answers in Scientific Computation (Fox, 1971)
L7	1/22/25	Gaussian elimination	AG 5.1-5.2	here
L8	1/23/25	Pivoting	AG 5.3	here
L9	1/24/25	Cholesky factorization	AG 5.5	here
L10	1/27/25	Coding demo - Cholesky and GE
L11	1/29/25	Coding demo - GEPP and GECP
L12	1/31/25	Exploiting sparsity	AG 5.6-5.7
L13	2/3/25	Least squares problems	AG 6.1-6.2
L14	2/5/25	Householder reflectors and QR factorization	AG 6.3
L15	2/7/25	Pseudoinverses and rank-deficient least squares	AG 8.2
L16	2/10/25	Gram-Schmidt and Modified Gram-Schmidt	AG 6.3
L17	2/12/25	Exploiting low-rank structure	None
L17	2/14/25	Special matrices	None
L18	2/17/25	Jacobi, Gauss-Seidel, and Richardson’s method	AG 7.1-7.2
L19	2/19/25	Gradient descent and applications to optimization	7.4
L20	2/21/25	Convergence of gradient descent	AG 7.4
L21	2/24/25	The conjugate gradient method, Krylov subspaces	AG 7.4-7.5
L22	2/26/25	Arnoldi process and GMRES	AG 7.5
L23	2/28/25	Lanczos process and MINRES, CG method	AG 7.4-7.5
L24	3/3/25	Preconditioning	AG 7.4
L25	3/3/25	Iterative methods for eigenvalues	AG 8.1
L26	3/3/25	Computing the SVD	AG 8.3