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I’ve been trying to understand matrix norms (full disclosure: school assignment, not looking for answers, just clarity!), and how they follow from vector norms – been awhile since I did much linear algebra, so i’m struggling a bit with the notation, in particular I’m solving in the general case that for matrix A (and any nonzero vector x)

$$\frac{||Ax||_1}{||x||_1} \le C$$

For C = maximum column sum of A

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The part I don’t think I understand is what ${||Ax||_1}$ actually means, relative to matrix A. Could someone help me to understand a bit better the notation, and to apply the matrix norm?

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Since it looks like you are interested in the matrix norm which is induced by a particular vector norm (take a look here) , there is a nice geometric interpretation for $\|A\|$:

$$\|A\|:=\max_{\|x\|=1}\|Ax\|,$$

that is, if you start with any unit vector, compute its image under the transformation $A$, and compute the norm of that image (which is a vector, so its computed via a vector norm), then $\|A\|$ is the largest such resulting value. In other words, $\|A\|$ is the maximum “stretch” that $A$ “does” to a unit vector.

As an example, suppose $A=\begin{bmatrix}1 & 2\\0 & 3\end{bmatrix}$, so $A:\mathbb{R^2}\to\mathbb{R}^2$, and we will consider $\mathbb{R}^2$ with the 2-norm. Then *the matrix norm induced by the (vector) 2-norm* described above is summarized graphically with this figure:

Note the unit vectors on the left and then some representative images under $A$. The length of the longest such image is $\|A\|$ (induced by this vector norm).

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