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

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Discussion Overview

The discussion centers around the concept of the pseudoinverse of a matrix, particularly focusing on its definition, properties, and the mathematical formulation as presented in linear algebra literature. The scope includes theoretical aspects and mathematical reasoning related to the pseudoinverse of non-square matrices.

Discussion Character

  • Exploratory, Technical explanation, Debate/contested

Main Points Raised

  • Cyrus inquires about the pseudoinverse, noting that it can be computed for nonsquare matrices and referencing a specific formulation from a linear algebra textbook.
  • Another participant suggests that for a non-square matrix A, one can set up the singular value decomposition such that the diagonal matrix D is square, allowing for the computation of the pseudoinverse.
  • This participant explains that while U and V are not square, their transposes can still be used in the formulation of the pseudoinverse.
  • A different participant questions the validity of taking the transpose of U and V, arguing that they are no longer orthogonal matrices after partitioning, which complicates the relationship between their inverses and transposes.

Areas of Agreement / Disagreement

Participants express differing views on the properties of U and V after partitioning, indicating a lack of consensus on the implications of their transposes in the context of the pseudoinverse.

Contextual Notes

The discussion highlights potential limitations in understanding the properties of U and V after partitioning, as well as the assumptions made regarding their orthogonality and the implications for the pseudoinverse formulation.

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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please help!
 
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.
 
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.
 
anyone can anwser my question please?
 

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