Proving Frobenius Norm of Matrix A

In summary, the conversation is about proving the Frobenius norm for an nxn matrix and showing that the norm of the sum of two matrices is less than or equal to the sum of their individual norms. There is some confusion about the formula for the norm of a vector and whether it is the Euclidean norm or the sum norm. The Schwartz inequality and the properties of absolute value are mentioned as possible methods for proving the request.
  • #1
cateater2000
35
0
Hi I'm in the process of proving a matrix norm. The Frobenius norm is defined by an nxn matrix A by ||A||_F=sum[(|aij|^2)^(1/2) i=1..n,j=1..n] I'm having trouble showing ||A+B|| <= ||A|| + ||B||

thanks for the help
 
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  • #2
What's the formula for the norm of a vector with n^2 entries?
 
  • #3
I have no idea could you enlighten me?
 
  • #4
Er... no offense, but you can't possibly be talking about matrix norms without already having learned vector norms.

What's the Euclidean norm of a 2-vector?
 
  • #5
the formula you gave looks wrong as well. i.e. you squared and then took square root before summinjg. so you are getting the "sum norm", whereas it seems you meant to get the "euclidean norm".

i think hurkyl is assuming you meant the euclidean norm, and then your formula would simply be the norm of a vector in euclidean n space. the properties of this norm are probably based on some inequality they teach at the beginnig of many courses called the schwartz inequality (see chapter 0 or 1 of spivak's calculus book). it is usually proven using the quadratic formula applied to a variable t times the variables x in the vector. i.e. use the fact that a quadratic equation has a solution if and only if the discriminant b^2 -4ac is non negative.

Actually with your formula, the sum norm, it is even easier to prove your request. indeed it seems obvious from the properties of absolute value. try it and see. of course your homework is now 3 months overdue so you are not reading this anymore.
 
  • #6
|ab|>=ab
2|ab|>=2ab
||a|+|b||>=|a+b|

Hope this helps:shy:
 

FAQ: Proving Frobenius Norm of Matrix A

1. What is the Frobenius norm of a matrix?

The Frobenius norm of a matrix is a measure of the size of the matrix, similar to the magnitude of a vector. It is defined as the square root of the sum of the squares of all the elements in the matrix.

2. How is the Frobenius norm of a matrix calculated?

The Frobenius norm of a matrix A is calculated using the formula ||A||F = sqrt(Σi,j |ai,j|2), where ai,j are the elements of matrix A.

3. What does proving the Frobenius norm of a matrix entail?

Proving the Frobenius norm of a matrix involves showing that the formula ||A||F = sqrt(Σi,j |ai,j|2) is a valid measure for the size of the matrix, and that it satisfies certain properties such as non-negativity, homogeneity, and submultiplicativity.

4. Why is the Frobenius norm of a matrix important?

The Frobenius norm of a matrix is important because it is a useful tool in various mathematical and computational applications. It can be used to measure the error between two matrices, as well as to determine the rank and condition number of a matrix. It also plays a role in matrix decomposition and optimization problems.

5. Can the Frobenius norm of a matrix be proven for any type of matrix?

Yes, the Frobenius norm of a matrix can be proven for any type of matrix, including real and complex matrices. The proof relies on basic properties of matrix algebra and does not require any specific assumptions about the matrix.

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