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MIT 18.065: Matrix Methods in Data Analysis, Signal Processing, and Machine Learning →0/14
1

Eckart-Young: The Closest Rank k Matrix to A

47:16
2

Norms of Vectors and Matrices

49:21
3

Four Ways to Solve Least Squares Problems

49:51
4

Survey of Difficulties with Ax = b

49:36
5

Minimizing "x" Subject to Ax = b

50:22
6

Computing Eigenvalues and Singular Values

49:28
7

Randomized Matrix Multiplication

52:24
8

Low Rank Changes in A and Its Inverse

50:34
9

Matrices A(t) Depending on t, Derivative = dA/dt

50:52
10

Derivatives of Inverse and Singular Values

43:08
11

Rapidly Decreasing Singular Values

50:34
12

Counting Parameters in SVD, LU, QR, Saddle Points

49:00
13

Saddle Points Continued, Maxmin Principle

52:13
14

Definitions and Inequalities

55:01

Low Rank Changes in A and Its Inverse

Gilbert StrangDistinguishedMIT

Professor

50:34·2019·graduate level

Professor Strang explores low rank matrices and the Sherman-Morrison-Woodbury formula for efficiently computing how small matrix changes impact their inverses.

MathematicsEngineeringLinear AlgebraSignal Processing & RoboticsLow Rank MatricesMatrix Inverse

MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018

In this lecture, Professor Strang introduces the concept of low rank matrices. He demonstrates how using the Sherman-Morrison-Woodbury formula is useful to efficiently compute how small changes in a matrix affect its inverse.

Source: MIT OpenCourseWare via YouTubeWatch on YouTube
LecturerAll lecturers →
Gilbert StrangDistinguished

MIT Professor Emeritus & Author of Introduction to Linear Algebra

MIT Professor of Mathematics Emeritus whose legendary 18.06 Linear Algebra and 18.085 Computational Science courses educated millions of engineers and mathematicians.

3 courses · 56 lecturesCanon
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Randomized Matrix Multiplication

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Matrices A(t) Depending on t, Derivative = dA/dt

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