Is this proof of an ##\infty## norm valid?

I am trying to prove
##||A||_{\infty} = max_i \sum_{j} |a_{ij}|##
which reads as the ##\infty## norm is the max row sum of matrix A.
##i## is the row index and ##j## is the column index.

Here is what I thought of:

##||A||_{\infty} = sup_{x\neq 0} \frac{||Ax||_{\infty}}{||x||_{\infty}}##
The numerator can be written as:
##||Ax||_{\infty} = max_i \sum_{j} |a_{ij} x_j| \leq max_i \sum_{j} |a_{ij}|\ |x_j| \leq max_j |x_j| * max_i \sum_{j} |a_{ij}| = ||x||_{\infty} * max_i \sum_{j} |a_{ij}|##
Therefore,
##||Ax||_{\infty} \leq ||x||_{\infty} * max_i \sum_{j} |a_{ij}|##
Next,
##\frac{||Ax||_{\infty}}{||x||_{\infty}} \leq max_i \sum_{j} |a_{ij}|##
Next,
the supremum is defined as the "least upper bound." Thus, the supremum of the above expression is simply the equal sign. Thus,
##||A||_{\infty} = max_i \sum_{j} |a_{ij}|##

Do you guys see any flaws in this proof? I saw various other proofs that went on to prove that the "<" condition cannot exist, which is still correct, but I don't understand what the point is when you know that the supremum is the "LEAST" upper bound.

andrewkirk
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The numerator can be written as:
##||Ax||_{\infty} = max_i \sum_{j} |a_{ij} x_j| \leq max_i \sum_{j} |a_{ij}|\ |x_j| \leq max_j |x_j| * max_i \sum_{j} |a_{ij}| = ||x||_{\infty} * max_i \sum_{j} |a_{ij}|##
The first equality is incorrect. However, it's fixable. Replace
$$||Ax||_{\infty} = max_i \sum_{j} |a_{ij} x_j|$$
by
$$||Ax||_{\infty} = max_i \left\vert \sum_{j} a_{ij} x_j\right\vert\leq max_i \sum_{j} |a_{ij} x_j|$$
I haven't looked through the rest yet. Will do that a bit later, but have to run off now.

The first equality is incorrect. However, it's fixable. Replace
$$||Ax||_{\infty} = max_i \sum_{j} |a_{ij} x_j|$$
by
$$||Ax||_{\infty} = max_i \left\vert \sum_{j} a_{ij} x_j\right\vert\leq max_i \sum_{j} |a_{ij} x_j|$$
I haven't looked through the rest yet. Will do that a bit later, but have to run off now.
Ah yes! Thanks. that is a typo, but the rest of the proof did not carry over that typo.

andrewkirk
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Next,
the supremum is defined as the "least upper bound."
That is correct.
Thus, the supremum of the above expression is simply the equal sign.
I'm afraid that doesn't make sense. A supremum is a number. An equals sign is not. Nor can I see any way of interpreting this statement to make it both meaningful and correct.

What you have proven (it needs a couple more steps added in, but you're close enough) is that
$$||A||_{\infty} \leq max_i \sum_{j} |a_{ij}|$$
Now you need to prove that
$$||A||_{\infty} \geq max_i \sum_{j} |a_{ij}|$$

Hint, use the definition of the infinity norm, and consider only vectors ##x## of norm 1. What's the biggest value of ##\|Ax\|_\infty## you can get given that ##x## has norm 1?

That is correct.

I'm afraid that doesn't make sense. A supremum is a number. An equals sign is not. Nor can I see any way of interpreting this statement to make it both meaningful and correct.

What you have proven (it needs a couple more steps added in, but you're close enough) is that
$$||A||_{\infty} \leq max_i \sum_{j} |a_{ij}|$$
Now you need to prove that
$$||A||_{\infty} \geq max_i \sum_{j} |a_{ij}|$$

Hint, use the definition of the infinity norm, and consider only vectors ##x## of norm 1. What's the biggest value of ##\|Ax\|_\infty## you can get given that ##x## has norm 1?
This is the part where I got stuck on.

I am not sure if it was the way I worded it, but I mean to say the supremum is the condition given by the equal sign, since it would be the "lowest" "upper" bound. Does that still not make sense?

##x\leq y##
sup(x) = y

so carried over to the current problem, wouldn't that make it the condition given by the equal sign?

i.e., ##sup_{x\neq 0} \frac{||Ax||_{\infty}}{||x||_{\infty}} = max_i \sum_{j} |a_{ij}|##

since we know the least upper bound of ##\frac{||Ax||_{\infty}}{||x||_{\infty}}## is ##max_i \sum_{j} |a_{ij}|##

andrewkirk
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Since we know ##||A||_{\infty}## is an upper bound for ##\frac{||Ax||_{\infty}}{||x||_{\infty}}##, to prove it's a least upper bound, all that's needed is to find a vector ##x## for which ##||A||_{\infty}=\frac{||Ax||_{\infty}}{||x||_{\infty}}##.

This is equivalent to finding a vector ##x## with unit infinity-norm, such that ##||A||_{\infty}=||Ax||_{\infty}##.
Can you think of such a unit vector? Since you want to make the vector as 'big' as possible, what's the 'biggest' vector you can think of that has unit infinity-norm (start by thinking of 'big' as meaning the size of the usual norm on ##\mathbb{R}^n##).

Since we know ##||A||_{\infty}## is an upper bound for ##\frac{||Ax||_{\infty}}{||x||_{\infty}}##, to prove it's a least upper bound, all that's needed is to find a vector ##x## for which ##||A||_{\infty}=\frac{||Ax||_{\infty}}{||x||_{\infty}}##.

This is equivalent to finding a vector ##x## with unit infinity-norm, such that ##||A||_{\infty}=||Ax||_{\infty}##.
Can you think of such a unit vector? Since you want to make the vector as 'big' as possible, what's the 'biggest' vector you can think of that has unit infinity-norm (start by thinking of 'big' as meaning the size of the usual norm on ##\mathbb{R}^n##).
Thank you. I think I know what I was missing. I basically used the end result to arrive st the end result which doesn't make much sense, since I am to prove the end result.

Are you saying x has to be a unit vector with an infinity norm equal to 1?
If so wouldn't that just be a vector with all zero elements except for one element which is 1?

andrewkirk
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Gold Member
Are you saying x has to be a unit vector with an infinity norm equal to 1?
If so wouldn't that just be a vector with all zero elements except for one element which is 1?
'UNit vector' just means a vector with norm equal to 1. Since all the norms being used here are infinity norms, that means a vector ##x## such that ##\|x\|_\infty=1##, which in turn means a vector for which the largest absolute value of any of its components is 1. For example, if ##n=3##, then (1 0 0), (1 1 0), (0 1 0), (0 -1 1), (1 1 1), (1 -1 1), (0.5 0 1), (0.5 -0.5 -1) are all unit vectors.

What I'm suggesting in my post is that you consider which of the vectors like this have the largest 2-norm, where the 2-norm of vector ##x## is the 'usual' (not infinity) norm calculation ##\|x\|_2\equiv\left(\sum_{k=1}^n x_k{}^2\right)^\tfrac{1}{2}##.

pyroknife
'UNit vector' just means a vector with norm equal to 1. Since all the norms being used here are infinity norms, that means a vector ##x## such that ##\|x\|_\infty=1##, which in turn means a vector for which the largest absolute value of any of its components is 1. For example, if ##n=3##, then (1 0 0), (1 1 0), (0 1 0), (0 -1 1), (1 1 1), (1 -1 1), (0.5 0 1), (0.5 -0.5 -1) are all unit vectors.

What I'm suggesting in my post is that you consider which of the vectors like this have the largest 2-norm, where the 2-norm of vector ##x## is the 'usual' (not infinity) norm calculation ##\|x\|_2\equiv\left(\sum_{k=1}^n x_k{}^2\right)^\tfrac{1}{2}##.
Oh I see.
Largest 2-norm would be (1,1,1), (-1, -1, -1), and so on.

So ##||x||_{\infty} = 1##
and
##||Ax||_{\infty} = max_i |\sum_{j} a_{ij}|##

is this the idea?

andrewkirk
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Gold Member
Yes, that's it!

Yes, that's it!
Thanks. Dumb question, but I am not seeing how this proves the infinite norm of a matrix.
I just showed ##||Ax||_{\infty} = max_i |\sum_j a_{ij}|##
but I am unsure how this relates to
##||A_{\infty}||##

I think I'm missing a simple connection here.

In particular, I am not understanding why we looked for a vector with a unit infinity norm that maximizes the 2-norm.

I am not understanding why we looked for a vector with a unit infinity norm that maximizes the 2-norm.

andrewkirk
Homework Helper
Gold Member
Above it was shown that, for all vectors ##x##, we have
$$\frac{||Ax||_{\infty}}{||x||_{\infty}} \leq \max_i \sum_{j} |a_{ij}|$$
So ##\max_i \sum_{j} |a_{ij}|## is an upper bound for ##\frac{||Ax||_{\infty}}{||x||_{\infty}}## over ##x##.
You have just identified a value of ##x##, namely ##x'\equiv (1 1 1)##, for which ##\frac{||Ax'||_{\infty}}{||x'||_{\infty}} = \max_i \sum_{j} |a_{ij}|##.

What does that tell you about whether there can be an upper bound ##u## for ##\frac{||Ax||_{\infty}}{||x||_{\infty}}## that is less than ##\max_i \sum_{j} |a_{ij}|##?

And what does that then tell you about what sort of an upper bound ##\max_i \sum_{j} |a_{ij}|## is?

Above it was shown that, for all vectors ##x##, we have
$$\frac{||Ax||_{\infty}}{||x||_{\infty}} \leq \max_i \sum_{j} |a_{ij}|$$
So ##\max_i \sum_{j} |a_{ij}|## is an upper bound for ##\frac{||Ax||_{\infty}}{||x||_{\infty}}## over ##x##.
You have just identified a value of ##x##, namely ##x'\equiv (1 1 1)##, for which ##\frac{||Ax'||_{\infty}}{||x'||_{\infty}} = \max_i \sum_{j} |a_{ij}|##.

What does that tell you about whether there can be an upper bound ##u## for ##\frac{||Ax||_{\infty}}{||x||_{\infty}}## that is less than ##\max_i \sum_{j} |a_{ij}|##?

And what does that then tell you about what sort of an upper bound ##\max_i \sum_{j} |a_{ij}|## is?
So I'm thinking, what if we have:
##
A = \begin{bmatrix}
1 & 1\\
1 & 1
\end{bmatrix}##

and ##
x =
\begin{bmatrix}
1\\
0
\end{bmatrix}##

We can see that ##||Ax||_{\infty} = 1## and ##||x||_{\infty} = 1##
thus ##\frac{||Ax||_{\infty}}{||x||_{\infty}}= 1##
but
##\max_i \sum_{j} |a_{ij}| = 2##
so the equality doesn't hold?

##\max_i \sum_{j} |a_{ij}| ## would always be greater or equal to ##\frac{||Ax||_{\infty}}{||x||_{\infty}}## for all ##x##

andrewkirk
Homework Helper
Gold Member
##\max_i \sum_{j} |a_{ij}| ## would always be greater or equal to ##\frac{||Ax||_{\infty}}{||x||_{\infty}}## for all ##x##
Yes, but that doesn't advance the proof at all, as you already proved that in the OP. The missing piece is to show that it's a least upper bound, not just any old upper bound. Focus on the two questions at the end of my post #15.

Yes, but that doesn't advance the proof at all, as you already proved that in the OP. The missing piece is to show that it's a least upper bound, not just any old upper bound. Focus on the two questions at the end of my post #15.
For the first question:
There can't be an upper bound ##u## less than ##\max_i \sum_j |a_{ij}|## Anything lower wouldn't be considered an "upper" bound.
For the second question:
Since anything lower is a no longer an "upper" bound, ##\max_i \sum_j |a_{ij}|## is simply the "least" upper bound.

I think these are the missing piece?

I still don't understand why we picked a unit vector with an infinity norm = 1 and a maximum 2-norm.
That is correct.

I'm afraid that doesn't make sense. A supremum is a number. An equals sign is not. Nor can I see any way of interpreting this statement to make it both meaningful and correct.

What you have proven (it needs a couple more steps added in, but you're close enough) is that
$$||A||_{\infty} \leq max_i \sum_{j} |a_{ij}|$$
Now you need to prove that
$$||A||_{\infty} \geq max_i \sum_{j} |a_{ij}|$$

Hint, use the definition of the infinity norm, and consider only vectors ##x## of norm 1. What's the biggest value of ##\|Ax\|_\infty## you can get given that ##x## has norm 1?

Also, for this post, how does the past previous posts prove that $$||A||_{\infty} \geq max_i \sum_{j} |a_{ij}|$$?

andrewkirk
$$||A||_{\infty} \geq max_i \sum_{j} |a_{ij}|$$