Eckart-Young: The Closest Rank k Matrix to A
47:16Norms of Vectors and Matrices
49:21Four Ways to Solve Least Squares Problems
49:51Survey of Difficulties with Ax = b
49:36Minimizing "x" Subject to Ax = b
50:22Computing Eigenvalues and Singular Values
49:28Randomized Matrix Multiplication
52:24Low Rank Changes in A and Its Inverse
50:34Matrices A(t) Depending on t, Derivative = dA/dt
50:52Derivatives of Inverse and Singular Values
43:08Rapidly Decreasing Singular Values
50:34Counting Parameters in SVD, LU, QR, Saddle Points
49:00Saddle Points Continued, Maxmin Principle
52:13Definitions and Inequalities
55:01Derivatives of Inverse and Singular Values
This lecture covers derivatives of inverse and singular values, then pivots to discuss LASSO optimization, the nuclear norm, matrix completion, and compressed sensing.
MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018
In this lecture, Professor Strang reviews how to find the derivatives of inverse and singular values. Later in the lecture, he discusses LASSO optimization, the nuclear norm, matrix completion, and compressed sensing.
Matrices A(t) Depending on t, Derivative = dA/dt
Next · Lecture 11Rapidly Decreasing Singular Values