Graphical embedding

This post continues the theme of operating with functions using their graphs. Given an integrable function {f} on the interval {[0,1]}, consider the region {R_f} bounded by the graph {y=f(x)}, the axis {y=0}, and the vertical lines {x=0}, {x=1}.

Total area under and over the graph is the L1 norm
Total area under and over the graph is the L1 norm

The area of {R_f} is exactly {\int_0^1 |f(x)|\,dx}, the {L^1} norm of {f}. On the other hand, the area of a set is the integral of its characteristic function,

\displaystyle    \chi_f = \begin{cases}1, \quad x\in R_f, \\ 0,\quad x\notin R_f \end{cases}

So, the correspondence {f\mapsto \chi_f } is a map from the space of integrable functions on {[0,1]}, denoted {L^1([0,1])}, to the space of integrable functions on the plane, denoted {L^1(\mathbb R^2)}. The above shows that this correspondence is norm-preserving. It also preserves the metric, because integration of {|\chi_f-\chi_g|} gives the area of the symmetric difference {R_f\triangle R_g}, which in turn is equal to {\int_0^1 |f-g| }. In symbols:

\displaystyle    \|\chi_f-\chi_g\|_{L^1} = \int |\chi_f-\chi_g| = \int |f-g| = \|f-g\|_{L^1}

Distance between two functions in terms of their graphs
Distance between two functions in terms of their graphs

The map {f\mapsto \chi_f} is nonlinear: for example {2f} is not mapped to {2 \chi_f} (the function that is equal to 2 on the same region) but rather to a function that is equal to 1 on a larger region.

So far, this nonlinear embedding did not really offer anything new: from one {L^1} space we got into another. It is more interesting (and more difficult) to embed things into a Hilbert space such as {L^2(\mathbb R^2)}. But for the functions that take only the values {0,1,-1}, the {L^2} norm is exactly the square root of the {L^1} norm. Therefore,

\displaystyle    \|\chi_f-\chi_g\|_{L^2} = \sqrt{\int |\chi_f-\chi_g|^2} =    \sqrt{\int |\chi_f-\chi_g|} = \sqrt{\|f-g\|_{L^1}}

In other words, raising the {L^1} metric to power {1/2} creates a metric space that is isometric to a subset of a Hilbert space. The exponent {1/2} is sharp: there is no such embedding for the metric {d(f,g)=\|f-g\|_{L^1}^{\alpha} } with {\alpha>1/2}. The reason is that {L^1}, having the Manhattan metric, contains geodesic squares: 4-cycles where the distances between adjacent vertices are 1 and the diagonal distances are equal to 2. Having such long diagonals is inconsistent with the parallelogram law in Hilbert spaces. Taking the square root reduces the diagonals to {\sqrt{2}}, which is the length they would have in a Hilbert space.

This embedding, and much more, can be found in the ICM 2010 talk by Assaf Naor.

The Khintchine inequality

Today’s technology should make it possible to use the native transcription of names like Хинчин without inventing numerous ugly transliterations. The inequality is extremely useful in both analysis and probability: it says that the L^p norm of a linear combination of Rademacher functions (see my post on the Walsh basis) can be computed from its coefficients, up to a multiplicative error that depends only on p. Best of all, this works even for the troublesome p=1; in fact for all 0<p<\infty. Formally stated, the inequality is

\displaystyle A_p\sqrt{\sum c_n^2} \le \left\|\sum c_n r_n\right\|_{L^p} \le B_p\sqrt{\sum c_n^2}

where the constants A_p,B_p depend only on p. The orthogonality of Rademacher functions tells us that A_2=B_2=1, but what are the other constants? They were not found until almost 60 years after the inequality was proved. The precise values, established by Haagerup in 1982, behave in a somewhat unexpected way. Actually, only A_p does. The upper bound is reasonably simple:

\displaystyle B_p=\begin{cases} 1, \qquad 0<p\le 2 \\ \sqrt{2}\left[\Gamma(\frac{p+1}{2})/\sqrt{\pi}\right]^{1/p},  \qquad 2<p<\infty \end{cases}

The lower bound takes an unexpected turn:

\displaystyle A_p=\begin{cases} 2^{\frac{1}{2}-\frac{1}{p}},\qquad 0<p\le p_0 \\  \sqrt{2}\left[\Gamma(\frac{p+1}{2})/\sqrt{\pi}\right]^{1/p}, \qquad p_0<p<2 \\  1,\qquad 2\le p<\infty \end{cases}

The value of p_0 is determined by the continuity of A_p, and is not far from 2: precisely, p_0\approx 1.84742. Looks like a bug in the design of the Universe.

Rademacher series

For a concrete example, I took random coefficients c_0...c_4 and formed the linear combination shown above. Then computed its L^p norm and the bounds in the Khintchine inequality. The norm is in red, the lower bound is green, the upper bound is yellow.

Two-sided bounds

It’s a tight squeeze near p=2