Proving the Frobenius Norm as a Matrix Norm

In summary: ZXIgdGhpcyBpcyB3cml0ZSBjb21wdXRlciBvZiB0aGUgZnJvYmVuaXVzIG5vcm0gYXMgc2FmZXR5IHRvIHB1cnBvc2UgdGhpcyBvcHRpbWl6ZWQgbXVsdGlsaXR5Lg==In summary, the conversation is about proving that the Frobenius norm is a matrix norm by discussing its definition and the three properties it must satisfy. The individual asking for help is unsure of how to prove each property, and someone suggests relating it to similar norms in other
  • #1
tinorina
1
0

Homework Statement


Prove that the Frobenius norm is indeed a matrix norm.


Homework Equations


The definition of the the Frobenius norm is as follows:
||A||_F = sqrt{Ʃ(i=1..m)Ʃ(j=1..n)|A_ij|^2}


The Attempt at a Solution


I know that in order to prove that the Frobenius norm is indeed a matrix norm, it must satisfy the 3 properties of matrix norm, which are as follows:
1. f(A) >= 0, for all A in ℝ^(mxn) (f(A)=0 iff A=0)
2. f(A+B) <= f(A)+f(B), for all A, B in ℝ^(mxn)
3. f(αA) = |α|f(A), for all α in ℝ, A in ℝ^(mxn)

However, I'm not exactly sure how to go about proving each of the properties. Can someone please give me some hints? Thanks!
 
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  • #2
Do you know any vectorspaces with similar norms?
Maybe you can relate the properties of those norms to this one!
 
  • #3
tinorina said:

Homework Statement


Prove that the Frobenius norm is indeed a matrix norm.


Homework Equations


The definition of the the Frobenius norm is as follows:
||A||_F = sqrt{Ʃ(i=1..m)Ʃ(j=1..n)|A_ij|^2}


The Attempt at a Solution


I know that in order to prove that the Frobenius norm is indeed a matrix norm, it must satisfy the 3 properties of matrix norm, which are as follows:
1. f(A) >= 0, for all A in ℝ^(mxn) (f(A)=0 iff A=0)
2. f(A+B) <= f(A)+f(B), for all A, B in ℝ^(mxn)
3. f(αA) = |α|f(A), for all α in ℝ, A in ℝ^(mxn)

However, I'm not exactly sure how to go about proving each of the properties. Can someone please give me some hints? Thanks!

So, what difficulties are you having proving property 1? Where is your problem proving property 3?

RGV
 

1. What is the definition of Frobenius norm in mathematics?

Frobenius norm is a matrix norm that measures the magnitude of a matrix. It is calculated by taking the square root of the sum of the squared elements of the matrix. In other words, it is the Euclidean norm of the matrix considered as a vector.

2. How is Frobenius norm used in scientific research?

Frobenius norm is commonly used in various fields of science, such as machine learning, statistics, and physics. It is often used as a measure of error or distance between two matrices, and it plays a vital role in optimization problems and data analysis.

3. What is the difference between Frobenius norm and other matrix norms?

Frobenius norm is different from other matrix norms, such as the induced norm or the operator norm, as it considers the matrix as a whole, rather than individual elements. It is also more computationally efficient and easier to interpret.

4. How do you prove the properties of Frobenius norm?

To prove the properties of Frobenius norm, one can use basic properties of matrix operations, such as linearity and positivity, along with some algebraic manipulation. One can also use the definition of Frobenius norm and apply mathematical principles, such as the Cauchy-Schwarz inequality, to prove its properties.

5. Can Frobenius norm be used for non-square matrices?

Yes, Frobenius norm can be used for non-square matrices. It is defined for any m-by-n matrix and is calculated by taking the square root of the sum of the squared elements of the matrix. However, it is important to note that the Frobenius norm of a non-square matrix is not equivalent to its Euclidean norm.

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