Is the Frobenius Norm a Reliable Indicator of Matrix Conditioning?

In summary, the conversation discusses the calculation of a matrix's Frobenius norm and the question of whether this indicates an ill-posed or well-posed matrix. The expert suggests that the ratio of the largest to smallest singular value is a better measure of a matrix's condition, and recommends reading through the first 7 chapters of "Linear Algebra Done Wrong" for a better understanding of these concepts. The person asking the question mentions a strange discrepancy between the Frobenius norm and the Max norm for a particular matrix.
  • #1
SeM
I have calculated that a matrix has a Frobenius norm of 1.45, however I cannot find any text on the web that states whether this is an ill-posed or well-posed indication. Is there a rule for Frobenius norms that directly relates to well- and ill-posed matrices?

Thanks
 
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  • #2
I assume by "ill posed" you mean poorly conditioned, i.e. potential numeric stability problems associated with inversion.

Like I said in your thread here:

https://www.physicsforums.com/threads/how-can-i-analyse-and-classify-a-matrix.931056/

it's the ratio of the largest singular value to smallest singular value that matters. I really struggle to imagine why you'd take that information and then ask a question about about adding up all the squared singular values, and asking what that sum (or the square root of that sum) tells us about condition number.
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Perhaps it'd be prudent for you to make your way through the first 7 chapters of Linear Algebra Done Wrong:

https://www.math.brown.edu/~treil/papers/LADW/LADW.html

It would walk you through many of the fundamentals needed to connect the dots between these different ideas.
 
  • #3
StoneTemplePython said:
I assume by "ill posed" you mean poorly conditioned, i.e. potential numeric stability problems associated with inversion.

Like I said in your thread here:

https://www.physicsforums.com/threads/how-can-i-analyse-and-classify-a-matrix.931056/

it's the ratio of the largest singular value to smallest singular value that matters. I really struggle to imagine why you'd take that information and then ask a question about about adding up all the squared singular values, and asking what that sum (or the square root of that sum) tells us about condition number.
- - - -
Not really need to go through an entire book for this. It's actually as comprehensive as you wrote here, which I was not aware of. However, the strange thing is that if the ratio between the smallest and largest values of the matrix is in the magnitude of 10^40, it defines an ill conditioned matrix, and the Max norm is very high, about 78. However, the Frobenius norm is "only" 1.5 , so I thought it was strange these two norm would deviate so much.
 

1. What is the Frobenius Norm of a matrix?

The Frobenius Norm of a matrix is a measure of the size or magnitude of the matrix. It is calculated by taking the square root of the sum of the squares of all the elements in the matrix.

2. How is the Frobenius Norm different from other matrix norms?

The Frobenius Norm is different from other matrix norms because it is a measure of the size of the matrix as a whole, while other norms such as the Euclidean Norm or Maximum Norm focus on specific properties of the matrix such as its largest element or its length as a vector.

3. What is the significance of the Frobenius Norm in mathematics and science?

The Frobenius Norm is used in a variety of mathematical and scientific fields, including linear algebra, signal processing, and statistics. It is often used as a measure of error or distance between matrices, and it also has applications in data analysis and machine learning.

4. How is the Frobenius Norm calculated in practice?

To calculate the Frobenius Norm of a matrix, you first square each element in the matrix, then sum all of those squared values. Finally, take the square root of the sum to get the Frobenius Norm.

5. Are there any properties or characteristics of the Frobenius Norm that are important to know?

Yes, there are several important properties of the Frobenius Norm, such as its submultiplicative property, which states that the norm of a product of matrices is less than or equal to the product of their individual norms. Additionally, the Frobenius Norm is always non-negative and becomes zero only if the matrix is a zero matrix.

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