Understanding the Pseudoinverse: What is it and How Does it Work?

  • Thread starter Cyrus
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In summary, the conversation discusses the concept of pseudoinverse and its relationship to singular value decomposition. The author of "Linear Algebra and its applications" explains how a rectangular matrix can still have a pseudo inverse by setting up the decomposition so that the diagonal matrix is square. The inverse of the diagonal matrix is then used to calculate the pseudo inverse of the original matrix. There is also a question raised about why the transposes of the orthogonal matrices are used in this process.
  • #1
Cyrus
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Hi, I need help with the pseudoinverse. I would like to know what it is and what it does. From what I've found on websites, you can find the inverse of a nonsquare matrix. My book, "Linear Algebra and its applications" by David Lay, says that the reduced singualr value decomposition can be written as:

[tex] A = U_r (D)V_r^T [/tex]

and that the pseudo inverse can be written as:

[tex] A^+ = V_r (D^-^1) U_r^T [/tex].

It looks as if the author simply did the inverse of A to get the pseudoinevrese. But how could he do that when A is rectangular?

Thank you,


Cyrus
 
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  • #2
please help!
 
  • #3
For non-square A, you can set up the decomposition so that D is square:
If A is 2 by 3, then U would have to be 2 by 3 but D and V would be 2 by 2.

You take the inverse of D but you only need the transposes of U and V.

Since the transpose of U would be 3 by 2, the pseudo-inverse of A, A+, would be 3 by 2.
 
  • #4
why do you take the transpose of U and V. They are no longer orthogonal matricies because we cut out some of them when we partitioned it. So it is no longer true that their inverse equals their transpose.
 
  • #5
anyone can anwser my question please?
 

1. What is the pseudoinverse?

The pseudoinverse is a mathematical concept that describes the inverse of a matrix that is not square or invertible. It allows for the solution of linear systems of equations that do not have unique solutions.

2. How is the pseudoinverse calculated?

The pseudoinverse is typically calculated using the Singular Value Decomposition (SVD) method. This involves decomposing the original matrix into three matrices and then using these values to calculate the pseudoinverse.

3. What are some applications of the pseudoinverse?

The pseudoinverse has many applications in fields such as signal processing, image processing, and data analysis. It is commonly used to find the least-squares solution to over-determined systems of equations or to solve for unknown variables in linear regression models.

4. Can the pseudoinverse be used to solve non-linear systems of equations?

No, the pseudoinverse can only be used to solve linear systems of equations. It is not applicable to non-linear systems.

5. How does the pseudoinverse differ from the traditional inverse of a matrix?

The traditional inverse of a matrix is only defined for square and invertible matrices, while the pseudoinverse can be calculated for any matrix. Additionally, the traditional inverse returns a unique solution, while the pseudoinverse can return multiple solutions.

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