Can the Induced Matrix Norm be Proven with Triangle Inequality?

In summary, the conversation discusses a statement about matrix norms and how to prove it. The speaker is unsure about which norms to use and considers using the triangle inequality but is unsure if it applies to matrix induced norms. Another speaker provides a hint by defining a maximizer for ||Ax|| and using induction to show that the inequality always holds. The conversation concludes with the first speaker gaining a better understanding of the problem.
  • #1
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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  • #2
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.
 
  • #3
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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  • #4
∥AX∥=∥A∥ I am not sure of this statement? A times X should give a vector, not a matrix A?
 
  • #5
This actually makes sense now thanks a lot!
 
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  • #6
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.
 
  • #7
That's good insight I hope to be able to work my way through problems like that some day
 

FAQ: Can the Induced Matrix Norm be Proven with Triangle Inequality?

What is an induced matrix norm?

An induced matrix norm is a mathematical concept used to measure the size or magnitude of a matrix. It is based on the idea that the norm of a matrix is influenced by the norms of its individual components, such as its rows or columns.

How is an induced matrix norm calculated?

The induced matrix norm is calculated by taking the maximum value of the product of the matrix and a vector with a unit norm. In other words, it is the largest possible value that the matrix can produce when multiplied by a unit vector.

Why is an induced matrix norm useful?

An induced matrix norm is useful because it provides a way to measure the size or magnitude of a matrix. This can be helpful in various applications, such as in analyzing the convergence of iterative algorithms or in determining the stability of a system.

How does an induced matrix norm relate to other matrix norms?

An induced matrix norm is a type of matrix norm that is based on the norms of the individual components of a matrix. It is closely related to other matrix norms, such as the Frobenius norm and the operator norm.

Can an induced matrix norm be negative?

No, an induced matrix norm cannot be negative. It is always a positive value, as it is based on the norms of the individual components of a matrix, which are also positive values.

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