Equivalent Norms: Proving $\|A\| \leq \|A\|_{Eucl} \leq \sqrt{n}\|A\|$

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In summary, the conversation discusses proving the inequality $\|A\| \leq \|A\|_{\text{Eucl}} \leq \sqrt{n}\|A\|$. The first inequality is shown using the Cauchy-Schwarz inequality, and the second inequality is proven using the fact that $\|A\|_{\text{Eucl}}^2 = \text{tr}(A^TA)$ and the continuity and monotonicity of the squaring function.
  • #1
mathmari
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Hey! :eek:

Let $A=(a_{i,j})$ a real matrix with $m$ rows and $n$ columns, $x\in \mathbb{R}^n$ and \begin{equation*}\|A\|:=\sup_{\|x\|_2\leq 1}\|Ax\|_2, \ \ \|A\|_{\text{Eucl}}:=\sqrt{\sum_{i=1}^m\sum_{j=1}^n|a_{i,j}|^2}\end{equation*}

I want to show that $$\|A\|\leq \|A\|_{\text{Eucl}} \leq \sqrt{n}\|A\|$$ I have already shown the first inequality:

Since $\displaystyle{(Ax)_i=\sum_{j=1}^na_{i,j}x_j}$, we get $\displaystyle{\|Ax\|_2^2=\sum_{i=1}^m\left (\sum_{j=1}^n|a_{i,j}x_j|\right )^2=\sum_{i=1}^m\left (\sum_{j=1}^n|a_{i,j}||x_j|\right )^2}$.

From the Cauchy–Schwarz inequality we get \begin{equation*}\left (\sum_{j=1}^n|a_{i,j}||x_j|\right )^2\leq \left (\sum_{j=1}^n{|a_{i,j}|^2}\right )\left (\sum_{j=1}^n|x_j|^2\right )=\left (\sum_{j=1}^n{|a_{i,j}|^2}\right )\|x\|_2^2\end{equation*}

Sp we get\begin{equation*}\|Ax\|_2^2=\sum_{i=1}^n\left (\sum_{j=1}^n|a_{i,j}||x_j|\right )^2\leq \sum_{i=1}^m\sum_{j=1}^n|a_{i,j}|^2\|x\|_2^2=\|A\|_{\text{Eucl}}^2\,\|x\|_2^2 \end{equation*}

Therefore we have that \begin{equation*}\|Ax\|_2\leq \|A\|_{\text{Eucl}}\,\|x\|_2\Rightarrow \sup_{\|x\|_2\leq 1}\|Ax\|_2\leq \sup_{\|x\|_2\leq 1}\|A\|_{\text{Eucl}}\,\|x\|_2=\|A\|_{\text{Eucl}}\end{equation*}

From that it implies that $\|A\|\leq \|A\|_{\text{Eucl}}$. Is everything correct? (Wondering)
Could you give me a hint for the second inequality? (Wondering)
 
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  • #2
mathmari said:
Hey! :eek:

Let $A=(a_{i,j})$ a real matrix with $m$ rows and $n$ columns, $x\in \mathbb{R}^n$ and \begin{equation*}\|A\|:=\sup_{\|x\|_2\leq 1}\|Ax\|_2, \ \ \|A\|_{\text{Eucl}}:=\sqrt{\sum_{i=1}^m\sum_{j=1}^n|a_{i,j}|^2}\end{equation*}

I want to show that $$\|A\|\leq \|A\|_{\text{Eucl}} \leq \sqrt{n}\|A\|$$ I have already shown the first inequality:
.
.
.
Is everything correct? (Wondering)
Yes!

mathmari said:
Could you give me a hint for the second inequality? (Wondering)
You could use the fact that $\|A\|_{\text{Eucl}}^2 = \text{tr}\,(A^TA)$ (where tr denotes the trace and $A^T$ is the transpose of $A$).
 
  • #3
Opalg said:
You could use the fact that $\|A\|_{\text{Eucl}}^2 = \text{tr}\,(A^TA)$ (where tr denotes the trace and $A^T$ is the transpose of $A$).

What do we get from that? I got stuck right now. (Wondering) Could we do also something like the following?
\begin{align*}\|A\|_{\text{Eucl}}^2=\sum_{i=1}^m\sum_{j=1}^n|a_{i,j}|^2=\sum_{j=1}^n\sum_{i=1}^m|a_{i,j}|^2=\sum_{j=1}^n\|a_j\|_2^2=\sum_{j=1}^n\|Ae_j\|_2^2\leq \sum_{j=1}^n\|A\|_2^2\|e_j\|_2^2=n\|A\|_2^2\end{align*} But instead of $\|A\|$ we have $\|A\|_2$. Is the above correct? If yes, can we get to $\|A\|$ ? (Wondering)
 
  • #4
Hi mathmari,

mathmari said:
Could we do also something like the following?
\begin{align*}\|A\|_{\text{Eucl}}^2=\sum_{i=1}^m\sum_{j=1}^n|a_{i,j}|^2=\sum_{j=1}^n\sum_{i=1}^m|a_{i,j}|^2=\sum_{j=1}^n\|a_j\|_2^2=\sum_{j=1}^n\|Ae_j\|_2^2\leq \sum_{j=1}^n\|A\|_2^2\|e_j\|_2^2=n\|A\|_2^2\end{align*} But instead of $\|A\|$ we have $\|A\|_2$. Is the above correct? If yes, can we get to $\|A\|$ ? (Wondering)

This idea will work, we just need to note two small things:

1) For a set $S$ of non-negative numbers -- which $S=\{\|Ax\|_{2} : \|x\|_{2}\leq 1\}$ is -- $\sup_{x\in S} x^{2}=\left(\sup_{x\in S} x\right)^{2}.$ This follows from the continuity and monotonicity of the squaring function $x\mapsto x^{2}$ on the non-negative half-line.

2) For each $e_{j}$, $\|Ae_{j}\|_{2}^{2}\leq \|A\|^{2}$, because, using point (1) above,
$$\|A\|^{2}=\left(\sup_{\|x\|_{2}\leq 1}\|Ax\|_{2} \right)^{2}=\sup_{\|x\|_{2}\leq 1}\|Ax\|^{2}_{2}\geq \|Ae_{j}\|^{2}_{2}.$$
 
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  • #5
GJA said:
This idea will work, we just need to note two small things:

1) For a set $S$ of non-negative numbers -- which $S=\{\|Ax\|_{2} : \|x\|_{2}\leq 1\}$ is -- $\sup_{x\in S} x^{2}=\left(\sup_{x\in S} x\right)^{2}.$ This follows from the continuity and monotonicity of the squaring function $x\mapsto x^{2}$ on the non-negative half-line.

2) For each $e_{j}$, $\|Ae_{j}\|_{2}^{2}\leq \|A\|^{2}$, because, using point (1) above,
$$\|A\|^{2}=\left(\sup_{\|x\|_{2}\leq 1}\|Ax\|_{2} \right)^{2}=\sup_{\|x\|_{2}\leq 1}\|Ax\|^{2}_{2}\geq \|Ae_{j}\|^{2}_{2}.$$

Ah I see! Thank you so much! (Yes)
 

Related to Equivalent Norms: Proving $\|A\| \leq \|A\|_{Eucl} \leq \sqrt{n}\|A\|$

1. What is the definition of an equivalent norm?

An equivalent norm is a different way of measuring the size or magnitude of a vector or matrix. It is defined as a norm that produces the same value for any given vector or matrix as the original norm.

2. How do you prove that two norms are equivalent?

To prove that two norms are equivalent, you must show that the two norms produce the same value for any given vector or matrix. This can be done by showing that the ratio between the two norms is bounded by a constant. In other words, if there exists a constant c such that for all vectors x, we have c * norm1(x) <= norm2(x) <= c * norm1(x), then the two norms are equivalent.

3. What is the significance of proving that two norms are equivalent?

Proving that two norms are equivalent allows us to use either norm interchangeably when performing calculations or solving problems. This can be useful when one norm is easier to work with than the other, or when different norms are better suited for different situations.

4. How does the Euclidean norm compare to other norms?

The Euclidean norm, also known as the 2-norm, is one of the most commonly used norms. It measures the distance from the origin to a point in space and is equivalent to the square root of the sum of squared elements in a vector. Other common norms include the 1-norm, which measures the sum of absolute values of vector elements, and the infinity norm, which measures the maximum absolute value of vector elements.

5. Why is it important to understand equivalent norms in linear algebra?

Equivalent norms play a significant role in linear algebra as they allow us to generalize properties and theorems across different norms. This enables us to use different norms to solve problems and prove results, making it a powerful tool in mathematical analysis. Additionally, understanding equivalent norms can also help us gain a deeper understanding of the underlying structure and properties of vector spaces.

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