What is the condition number of a rectangular matrix and how can it be finite?

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Homework Help Overview

The discussion revolves around the condition number of a rectangular matrix and its implications, particularly in the context of singular value decomposition (SVD). The original poster expresses confusion regarding the finite condition number returned by computational tools for rectangular matrices, which are typically considered non-invertible.

Discussion Character

  • Conceptual clarification, Assumption checking

Approaches and Questions Raised

  • Participants explore the relationship between the rank of a matrix and its singular values, questioning the implications of having a finite condition number for a rectangular matrix. There is also a discussion about numerical roundoff error and its impact on the singular values computed in software.

Discussion Status

The discussion is ongoing, with participants providing insights into the nature of singular values in rectangular matrices and the potential for numerical errors in computational results. References to external materials and textbooks are suggested to further clarify the concepts involved.

Contextual Notes

There is mention of varying interpretations regarding the conditions under which singular values may be zero, depending on the dimensions of the matrix. Additionally, the original poster indicates a lack of information in their current textbook regarding these topics.

Niles
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Homework Statement


Hi

If we have a matrix M, we can always make a singular value decomposition. If the matrix has full column rank (= is invertible), then the singular values are all nonzero, otherwise they are not all nonzero.

Now, we can also associate a condition number to a matrix given by

cond(M) = s1/sk

where k is min(m, n) (where M is a m times n matrix). If M is rectangular, then it is not invertible, so the condition number should be infinite.

Now, when I find the condition number of a rectangular matrix in MatLAB, it gives me a (large) number, which is finite. How can that be?
 
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The reason is numerical roundoff error. If you repeat the exercise with random values in the matrix, the last singular value will sometimes be zero but very very small most of the time. For this reason some commerical programs allow the user to specific a threshold for what is considered zero.

Also, I think the statement about rectangular matrices needs to be clarifed. If the matrix has more rows than columns and is full rank it won't have a zero singular value. If the number of rows is less than the number of columns it will have a zero singular value.
 
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Do you know a good reference on SVD? I didn't know the last part you stated, becaus it isn't in my book.
 
Niles said:
Do you know a good reference on SVD? I didn't know the last part you stated, becaus it isn't in my book.

Not really, but the following link (see page 3) shows an example of a rectangular matrix with all non zero singular values.

http://www.farinhansford.com/books/pla/material/pseudo.pdf
 
Last edited by a moderator:
Niles said:
Do you know a good reference on SVD? I didn't know the last part you stated, becaus it isn't in my book.

Gilbert Strang, Linear Algebra.
I learned a ton from it.
 

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