Dismiss Notice
Join Physics Forums Today!
The friendliest, high quality science and math community on the planet! Everyone who loves science is here!

When is the Frobenius norm of a matrix equal to the 2-norm of a matrix?

  1. Apr 22, 2012 #1
    What conditions most be true for these two norms to be equal? Or are they always equal?
     
  2. jcsd
  3. Apr 22, 2012 #2


    I'm far from being a specialist in this, but it seems to me that "Frobenius norm of a matrix" is just the name given to the 2-norm...

    Don
     
  4. Apr 22, 2012 #3
    Well, in the applied linear algebra course I'm taking currently, the Frobenius norm of a matrix A is defined as the square root of the trace of A'A and the 2-norm is defined as the square root of the largest eigenvalue of A'A. I'm just not sure if they're always the same.
     
  5. Apr 22, 2012 #4
    The Frobenius and 2-norm of a matrix coincide if and only if the matrix has rank 1 (i.e. if and only if the matrix can be represented as A=c r, where r is a row and c is a column).

    You can see that from the fact that Frobenius norm is [itex]\left( \sum_k s_k^2\right)^{1/2}[/itex] and the 2-norm is [itex]\max s_k[/itex], where [itex]s_k[/itex] are singular values. So equality happens if and only if there is only one non-zero singular value, which is equivalent to the fact that the rank is 1.
     
  6. Apr 22, 2012 #5
    Excellent, thank you. The matrix in a proof I'm working on involves a rank 1 matrix, so this equality of the two norms applies perfectly.
     
  7. Apr 23, 2012 #6

    AlephZero

    User Avatar
    Science Advisor
    Homework Helper

    More generally, ##||A||_2 \le ||A||_F \le \sqrt{r}||A||_2## where r is the rank of A.
     
  8. Dec 12, 2013 #7
    May you shed some light on this? Or quote any possible reference? Thanks
     
  9. Dec 13, 2013 #8

    jbunniii

    User Avatar
    Science Advisor
    Homework Helper
    Gold Member

    Assuming you accept Hawkeye18's formulas, namely
    $$\|A\|_F = \left( \sum_k s_k^2\right)^{1/2}$$
    and
    $$\|A\|_2 = \max{s_k}$$
    then we have
    $$\|A\|_2 = \max{s_k} = \left( (\max{s_k})^2\right)^{1/2} \leq \left( \sum_{k} s_k^2 \right)^{1/2} = \|A\|_F$$

    For the second inequality, note that the rank of ##A## is precisely the number of nonzero singular values. Let's sort the singular values so that the nonzero ones all come first. Then for a rank ##r## matrix, we have
    $$\|A\|_F = \left( \sum_{k=1}^{r} s_k^2\right) ^{1/2} \leq \left( \sum_{k=1}^{r} (\max s_k)^2 \right)^{1/2} = (r (\max s_k)^2)^{1/2} = \sqrt{r} \|A\|_2$$
    Equality holds if and only if the ##r## nonzero singular values are all equal.
     
  10. Jan 8, 2015 #9
    when matrix A is Singular which means det(A)=0.
     
Know someone interested in this topic? Share this thread via Reddit, Google+, Twitter, or Facebook




Similar Discussions: When is the Frobenius norm of a matrix equal to the 2-norm of a matrix?
  1. Invariant Matrix norm (Replies: 1)

Loading...