How to Calculate the Pseudoinverse Using the SVD?

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Discussion Overview

The discussion revolves around the calculation of the pseudoinverse using Singular Value Decomposition (SVD). Participants explore the components of SVD, specifically the matrices U and Vt, and the process of obtaining the pseudoinverse from these matrices. The conversation includes technical clarifications and some confusion regarding the definitions and calculations involved.

Discussion Character

  • Technical explanation
  • Conceptual clarification
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant expresses uncertainty about the question's requirements, specifically regarding the values of x1, x2, x3, and x4.
  • Multiple participants suggest rechecking the determinant related to the expression ##~\mathbf{H^T H - I} \it \lambda ##.
  • There is a request for clarification on the meaning of the matrix U and how to derive it along with Vt.
  • One participant defines U as a unitary matrix and provides a link for further reading, noting the mathematical property of U being its own inverse when complex.
  • Another participant explains that U is part of the SVD representation and discusses the notation differences between Σ and S.
  • There is a claim that an image referenced in the discussion incorrectly presents the pseudoinverse calculation, suggesting it should be VS+UT instead of US+VT.
  • A question is raised about how to convert the diagonal matrix S to its pseudoinverse S+.
  • A response provides a method for obtaining the pseudoinverse of a rectangular diagonal matrix, emphasizing the treatment of small elements in numerical computation.

Areas of Agreement / Disagreement

Participants express varying levels of understanding and agreement on the definitions and calculations related to SVD and the pseudoinverse. There is no clear consensus on the correct representation of the pseudoinverse calculation, indicating ongoing debate.

Contextual Notes

Some participants have corrected their earlier mistakes, but there remain unresolved questions about the definitions and calculations of the matrices involved in SVD and the pseudoinverse. The discussion also highlights potential ambiguities in the mathematical expressions referenced.

nao113
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Homework Statement
Hello, I have question related to SVD. Can anyone give me hints about what should I do to solve this question. I provided the question and my answer on the picture below. I have found the eigenvalues and eigenvector but I m not sure whether it is correct not and then whether this answer already cover the question. Thank you
Relevant Equations
𝐀=𝐔Σ𝐕∗
Screen Shot 2022-05-21 at 16.05.23.png


My Answer:
I am still beginner in this area so it s quite hard for me to understand this one. I am not sure what the output that this question asked me. I thought it might be asked about the value of x1, x2, x3, and x4

WhatsApp Image 2022-05-22 at 4.32.31 PM.jpeg
 
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Recheck that determinant for ##~\mathbf{H^T H - I} \it \lambda ## .
 
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SammyS said:
Recheck that determinant for ##~\mathbf{H^T H - I} \it \lambda ## .
Hello, thank you the respond, actually, I have correct my mistakes here. I already got the answer from this. But still, can you help me to understand what is U means? and how to get the matrix for it as well as Vt?
 

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nao113 said:
But still, can you help me to understand what is U means?
U is a unitary matrix. See https://en.wikipedia.org/wiki/Unitary_matrix.

If U is a complex square matrix, it is unitary if its complex transpose (U*) is also its inverse. I.e., U* U = U U* = I.
 
nao113 said:
Hello, thank you the respond, actually, I have correct my mistakes here. I already got the answer from this. But still, can you help me to understand what is U means? and how to get the matrix for it as well as Vt?
U is one of the parts of the SVD, which is given by U Σ VT.
See: https://en.wikipedia.org/wiki/Singular_value_decomposition
Sometimes the letter S is used in place of Σ, which is the case in the image you've linked to.

The image you linked is trying to show how to calculate the pseudoinverse using the SVD: https://en.wikipedia.org/wiki/Moore–Penrose_inverse. See the heading about 2/3 of the way down the page: "Singular value decomposition (SVD)"

but they have made a mistake. They show the pseudoinverse as: US+VT, but it should be VS+UT

The numeric values they show are correct.
 
The Electrician said:
U is one of the parts of the SVD, which is given by U Σ VT.
See: https://en.wikipedia.org/wiki/Singular_value_decomposition
Sometimes the letter S is used in place of Σ, which is the case in the image you've linked to.

The image you linked is trying to show how to calculate the pseudoinverse using the SVD: https://en.wikipedia.org/wiki/Moore–Penrose_inverse. See the heading about 2/3 of the way down the page: "Singular value decomposition (SVD)"

but they have made a mistake. They show the pseudoinverse as: US+VT, but it should be VS+UT

The numeric values they show are correct.
do you know how to change S to S+?
 
nao113 said:
do you know how to change S to S+?
Re-read the section I linked to:

The image you linked is trying to show how to calculate the pseudoinverse using the SVD: https://en.wikipedia.org/wiki/Moore–Penrose_inverse. See the heading about 2/3 of the way down the page: "Singular value decomposition (SVD)"

"For a rectangular diagonal matrix such as S, we get the pseudoinverse by taking the reciprocal of each non-zero element on the diagonal, leaving the zeros in place, and then transposing the matrix. In numerical computation, only elements larger than some small tolerance are taken to be nonzero, and the others are replaced by zeros." Any of the elements that are very small (comparable to to the value of epsilon in the arithmetic of the math system you're using) are replaced by zero, not reciprocated."
 
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