Can the Induced Matrix Norm be Proven with Triangle Inequality?

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SUMMARY

The discussion centers on proving the inequality $$||A^k|| \leq ||A||^{k}$$ for a matrix A and a positive integer k, utilizing induced matrix norms. Participants clarify that the norm of a matrix is defined as the supremum of the norm of Ax over the norm of x, specifically under the assumption of Euclidean norms. The conversation emphasizes the application of the triangle inequality in this context, concluding that the inequality holds through induction and the properties of induced norms.

PREREQUISITES
  • Understanding of induced matrix norms
  • Familiarity with the triangle inequality in normed spaces
  • Knowledge of matrix operations and properties
  • Basic concepts of supremum and maximizers in mathematical analysis
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  • Study the properties of induced matrix norms in detail
  • Learn how to apply the triangle inequality in various mathematical contexts
  • Explore induction proofs specifically related to matrix inequalities
  • Investigate the relationship between different types of norms, including Euclidean and induced norms
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Mathematicians, students of linear algebra, and anyone interested in understanding matrix norms and their properties in mathematical proofs.

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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