Video Lectures

Lecture 14: Low Rank Changes in A and Its Inverse

Description

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.

Summary

If A is changed by a rank-one matrix, so is its inverse.
Woodbury-Morrison formula for those changes
New data in least squares will produce these changes.
Avoid recomputing over again with all data
Note: Formula in class is correct in the textbook.

Related section in textbook: III.1

Instructor: Prof. Gilbert Strang

Problems for Lecture 14
From textbook Section III.1

1. Another approach to (IuvT)1 starts with the formula for a geometric series:
(1x)1=1+x+x2+x3+   Apply that formula when x=uvT= matrix:

(IuvT)1=I+uvT+uvTuvT+uvTuvTuvT+=I+u[1+vTu+vTuvTu+]vT

Take x=vTu  to see I+uvT1vTu. This is exactly equation (1) for (IuvT)1.

4. Problem 3 found the inverse matrix M1=(AuvT)1. In solving the equation My=b, we compute only the solution y and not the whole inverse matrix M1. You can find y in two easy steps:

Step 1:   Solve Ax=b and Az=u. Compute D=1vTz.

Step 2:   Then y=x+vTxDz is the solution to My=(AuvT)y=b.

Verify (AuvT)y=b. We solved two equations using A, no equations using M.

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Spring 2018
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