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

In summary: One of the things it talks about is that if a matrix has full column rank, the singular values are all nonzero. If the matrix doesn't have full column rank, then some of the singular values might not be nonzero.
  • #1
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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  • #2
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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  • #3
Do you know a good reference on SVD? I didn't know the last part you stated, becaus it isn't in my book.
 
  • #4
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
 
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  • #5
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.
 

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

1. What is Singular Value Decomposition (SVD)?

Singular Value Decomposition (SVD) is a mathematical technique used to factorize a rectangular matrix into three separate matrices - U, Σ, and V. This factorization allows us to reduce the dimensionality of the original matrix while preserving the most important information.

2. What is the purpose of SVD?

The purpose of SVD is to reduce the number of features or dimensions in a dataset while retaining the most important information. This can help with data compression, visualization, and noise reduction.

3. How is SVD different from other matrix factorization techniques?

SVD is different from other matrix factorization techniques because it can be applied to any type of matrix, including non-square matrices and matrices with missing values. It also produces the most accurate low-rank approximation of the original matrix.

4. What are the applications of SVD?

SVD has many applications in various fields such as data analysis, image processing, natural language processing, and recommendation systems. It is also used in machine learning algorithms such as principal component analysis (PCA) and collaborative filtering.

5. What are the limitations of SVD?

One limitation of SVD is that it can be computationally expensive, especially for large matrices. It also assumes that the data is linearly related, which may not always be the case. Additionally, SVD may not work well with matrices that have high levels of noise or outliers.

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