## No Similarity-Invariant Matrix Norm

A matrix norm on M_{n} is said to be *weakly unitarily-invariant* if conjugating a matrix by a unitary U does not change the norm. That is,

Many commonly-used matrix norms are weakly unitarily-invariant, including the operator norm, Frobenius norm, numerical radius, Ky Fan norms and Schatten p-norms. One might naturally wonder whether there are matrix norms that satisfy the slightly stronger property of *similarity-invariance*:

Upon first glance there doesn’t seem to be any reason why this shouldn’t be possible — one can look for simple examples that cause problems, but you’ll have trouble coming up with a matrix that causes problems if you restrict your attention to “nice” (i.e., normal) matrices. Nevertheless, we have the following lemma, which appeared as Exercise IV.4.1 in [1]:

**Lemma (No Similarity-Invariant Norm).** Let f : M_{n} → **R** be a function satisfying f(SXS^{-1}) = f(X) for all X,S ∈ M_{n} with S invertible. Then f is not a norm.

If you’re interested in the (very short and elementary) proof of this lemma, see the pdf attached below. I would be greatly interested in seeing a proof of this fact that relies less on the structure of matrices themselves. It seems as though there should be a more general result that characterizes when we can and can not find a norm on a given vector space that is invariant with respect to some given subgroup, or some such thing. Would anyone care to enlighten me?

**Related Links:**

**References:**

- R. Bhatia,
*Matrix analysis*. Volume 169 of Graduate texts in mathematics (1997).

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