# “Scaled $L^p$ norm” and geometric mean

The $L^p$ norm in $\mathbb{R}^n$ is \begin{align} \|x\|_p = \left(\sum_{j=1}^{n} |x_j|^p\right)^{1/p}. \end{align} Playing around with WolframAlpha, I noticed that, if we define the "scaled" $L^p$ norm in $\mathbb{R}^n$ to be

\begin{align} \| x \|_p = \left(\frac{1}{n}\sum_{j=1}^{n} |x_j|^p\right)^{1/p} \end{align} then \begin{align} \lim_{p \to 0} \|x\|_p &= \left( \prod_{j=1}^{n} |x_j| \right)^{1/n}, \end{align} which is the geometric mean of the coordinates' absolute values. This is interesting maybe because the $L^p$ norm doesn't have a nice limit at zero.

My questions:

1. How do I prove this?
2. Is this definition of "scaled $L^p$ norm" interesting, or known by another name, or used anywhere?
3. Is there any interesting reason to define the $L^0$ norm as the geometric mean, as above?
4. Further reading?

Thanks!

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## 2 Answers

I'll prove the general case in which $\mu$ is a positive measure on a space $X$ and $\mu(X) = 1$. Your particular case follows by setting $X = \{1, \ldots, n\}$ and $\mu(i) = 1/n$.

By definition: $$\|f\|_p = \left\{\int_X |f|^p \,d\mu\right\}^{1/p}$$

Lemma 1: If $0 < r < s < 1$, then $\|f\|_r \le \|f\|_s$.

Proof: $\varphi(x) = x^{s/r}$ is a convex function. Apply Jensen's inequality to $\int_X |f|^r \,d\mu$ to get: $$\left\{\int_X |f|^r \,d\mu\right\}^{s/r} \le \int_X |f|^s \,d\mu$$

Hence $\|f\|_r \le \|f\|_s$.

Lemma 2: If $0 < p < 1$, then $\int_X \log|f| \,d\mu \le \log \|f\|_p$.

Proof: $\log$ is a concave function. Apply Jensen's inequality to $\int_X |f|^p \,d\mu$ to get the desired inequality.

From lemmas 1 and 2, it follows that $\log\|f\|_{1/n}$ is decreasing and bounded from below. Therefore, it converges as $n \to \infty$.

To find the limit, apply the inequality $\log a \le a - 1$ with $a = \int_X |f|^{1/n} \,d\mu$ to get: $$\log \|f\|_{1/n} \le \int_X \frac{|f|^{1/n} - 1}{1/n} \,d\mu \tag{1}$$

Use L'Hôpital's rule to obtain $\lim_{x \to 0} \dfrac{a^x - 1}{x} = \log a$. Take the limit of (1) as $n \to \infty$ and apply the dominated convergence theorem to get: $$\lim_{n \to \infty} \log \|f\|_{1/n} \le \int_X \log|f| \,d\mu$$

Apply the squeeze theorem with lemma 2 to obtain: $$\lim_{n \to \infty} \log \|f\|_{1/n} = \int_X \log|f| \,d\mu$$

Since $\log$ is continuous, we conclude: $$\lim_{n \to \infty} \|f\|_{1/n} = \exp\left(\int_X \log|f| \,d\mu\right)$$

To answer your other questions, the "scaled norm" follows from the general case as I explained at the beginning of my answer. I've never seen the geometric mean called $L^0$. As for further readings, check out Rudin's Real and Complex Analysis or Folland's Real Analysis. The above is an exercise in one of them (I think the former).

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By the way, if you're only familiar with $L^p$ norms and Jensen's inequality in finite spaces, you can follow the same proof by replacing integrals with sums. Try it! – Ayman Hourieh Jan 19 at 22:40
Sorry for the delay, but thanks for your answer, your proof was very clear and easy to follow! – usul Jan 25 at 15:14

Scaled $L^p$ norm (or rather $\ell^p$, since you work with vectors) is known as Generalized mean. A bunch of interesting inequalities involving the means are found in the book Inequalities by Hardy, Littlewood, and Pólya.

The integral geometric mean $\exp\left(\frac{1}{2\pi}\int_0^{2\pi} \log f(\theta)\,d\theta \right)$ comes up in complex analysis, especially as it relates to operator theory and involves the name of Gabor Szegő. See the terse Wikipedia article on Szegő limit theorems and the not-at-all-terse book by Barry Simon Szego's Theorem and Its Descendants

In a visit back to his native Budapest, Pólya mentioned this conjecture to Szegő, then an undergraduate, and he proved the theorem below, published in 1915... At the time, Szegő was nineteen, and when the paper was published, he was serving in the Austrian Army in World War I

The book Banach spaces of analytic functions by Kenneth Hoffman presents this topic from the viewpoint of complex analysis without much operator theory. The quantity $\exp\left(\frac{1}{2\pi}\int_0^{2\pi} \log f(\theta)\,d\theta \right)$ turns out to be equal to $\inf_{p}\int|1-p|^2 f(\theta)\,d\theta$ where $p$ runs over all polynomials vanishing at $0$. In particular, this gives a criterion for the density of polynomials in weighted $L^2$ spaces.

In a rather different direction, the integral geometric mean comes up in number theory. If $p$ a complex polynomial, the quantity $\exp\left(\frac{1}{2\pi}\int_0^{2\pi} \log |p(\theta)|\,d\theta \right)$ is called the Mahler measure of $p$, denoted $M(p)$. Lehmer's conjecture asserts that there is a gap $(1,\mu)$ in the possible values of $M(p)$: that is, either $M(p)=1$ or $M(p)\ge \mu>1$. Conjecturally, $\mu$ is attained by the polynomial $$p(z)= z^{10}-z^9+z^7-z^6+z^5-z^4+z^3-z+1$$ But even the existence of such $\mu$ remains unknown, let alone its precise value. The Wikipedia article has a good list of references.

That said, please do not call the integral geometric mean "the $L^0$ norm". This term is ambiguous and misleading enough as it is.

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Thanks very much, this was very interesting! – usul Jan 25 at 15:14
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