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
55:01Survey of Difficulties with Ax = b
This session addresses the computational challenges encountered when solving the matrix equation Ax=b, particularly with large-scale matrices.
MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018
The subject of this lecture is the matrix equation Ax=b. Solving for x presents a number of challenges that must be addressed when doing computations with large matrices.
Four Ways to Solve Least Squares Problems
Next · Lecture 5Minimizing "x" Subject to Ax = b