Can the Induced Matrix Norm be Proven with Triangle Inequality?

AI Thread Summary
The discussion revolves around proving the inequality ||A^k|| ≤ ||A||^k for a matrix A and a positive integer k, using induced matrix norms. Participants explore the definitions of matrix norms and the implications of the triangle inequality, questioning whether it applies to induced norms. There is uncertainty about the maximizers for the norms of Ax and A^k, and how they relate to each other. The conversation highlights the importance of maintaining the supremum definition for proper proof. Overall, the participants are seeking clarity on the relationship between matrix operations and induced norms.
FOIWATER
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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).
 
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What if the x that gives the sup for A^k is not the same as the x that is the sup for A? A acting on any vector will always be less than or equal to its sup.
 
To be more clear...
Let ##X## be your maximizer for ##\|Ax\|##. Then
##\| A^k \| = \| A A A ... A \| = \sup_{\|x\|=1} \left( \| A A A ... Ax \| \right)##
Let ##x_0 ## be any x such that ##\|x_0 \| = 1##.
Then you know that ##\|Ax_0\| \leq \|AX\|=\|A\|. ##
Let ##x_1 = Ax_0##. By similar argument, ##\|A\frac{x_1}{\|x_1\|}\|=\frac{\|Ax_1\|}{\|x_1\|} \leq \|AX\|\implies \|Ax_1\|\leq \|AX\|\|x_1\| \leq \|AX\|\|A\|=\|A\|^2 .##
And by induction, you can clearly see that no matter what, you won't be able to break the inequality.
 
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∥AX∥=∥A∥ I am not sure of this statement? A times X should give a vector, not a matrix A?
 
This actually makes sense now thanks a lot!
 
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FOIWATER said:
∥AX∥=∥A∥ I am not sure of this statement? A times X should give a vector, not a matrix A?
Right, but I defined ||AX|| to be ||A|| earlier.
RUber said:
Let X be your maximizer for ∥Ax∥
Of course, that is assuming that there is a maximum. You might have to keep the definition of ||A|| as the supremum to be proper...but the logic is the same.
 
That's good insight I hope to be able to work my way through problems like that some day
 
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