- #1
FOIWATER
Gold Member
- 434
- 12
Hi, I found a statement without a proof. It seems simple enough, but I am having trouble proving it because I am not positive about induced matrix norms. The statement is that $$||A^k|| \leq||A||^{k}$$ for some matrix A and positive integer k. I have found that the norm of a matrix is the supremum of the norm of Ax over the norm of x, but I do not know to which norms these refer?
I am assuming euclidean norms. Since Ax gives us back a vector and x is itself a vector.
So I have that:
$$||A^k|| = \sup_{||x||=1}(||A^{k}x|| : ||x||=1)$$
and
$$||A||^{k} = \sup_{||x||=1}(||Ax|| : ||x||=1)^{k}$$
Not sure what to do with them, though, any hints appreciated. I was thinking triangle inequality.. but I didn't really get anything from it. And I do not know if the triangle inequality applies to this matrix induced norm (although I think it applies to any operation that qualifies as a norm, since it defines norms).
I am assuming euclidean norms. Since Ax gives us back a vector and x is itself a vector.
So I have that:
$$||A^k|| = \sup_{||x||=1}(||A^{k}x|| : ||x||=1)$$
and
$$||A||^{k} = \sup_{||x||=1}(||Ax|| : ||x||=1)^{k}$$
Not sure what to do with them, though, any hints appreciated. I was thinking triangle inequality.. but I didn't really get anything from it. And I do not know if the triangle inequality applies to this matrix induced norm (although I think it applies to any operation that qualifies as a norm, since it defines norms).