Let $f \colon \mathbb R^N \to \mathbb R$ be a smooth function. Let $\mu$ be a probability measure on $[0,1]$ and $X_1, \ldots , X_N$ be i.i.d. random variables on $\mathbb R$.

Question 1. What is the maximum value of the expectation $$ \mathbb E[\vert f(X_1, \ldots , X_N) \vert] = \int_{\mathbb [0,1]^N} \vert f(x_1, \ldots , x_N) \vert d\mu(x_1) \ldots d\mu(x_N) $$ among all probability measures $\mu$ on $[0,1]$?

This question arises from this post and from this recent answer of Sangchul Lee which seem however tailored for specific functions $f$ and particularly for the case $N=2$.

I am very interested in the case $N\ge 3$; the function $f$ maybe be as smooth as needed (e.g. a polynomial). I have troubles in extending the (very elegant) variational approach of Sangchul Lee's to more variables, as no "bilinear form" is available.

After Nate Eldredge's comment, it is not restrictive to consider only a.c. measures. Furthermore, we can initially consider a somehow easier case, where $f$ is a polynomial (then maybe work by uniform approximation). In addition, thanks to a clever "random thought" due to Pierre PC (see comments below), we can start assuming symmetry of the integrand. All in all, the problem "boils down" to the following:

Question 1 (easier version): Maximize $$ \int_{[0,1]^N} p(x_1, \ldots, x_N) g(x_1) g(x_2) \ldots g(x_N) dx_1 dx_2 \ldots dx_N, $$ among functions $g \ge 0$ such that $\int_0^1 g(s)\, ds = 1$, being $p \in \mathbb R[x_1, \ldots , x_N]$ a symmetric polynomial (of constant sign on [0,1]?).

Can this be handled by the general Holder's inequality?

Since the general case seems too hard, let me propose a toy model we can begin with. To me, it seems the difficult point is not the integrand (in view of the comment above general reduction arguments should lead to a very simple form of it) but rather the fact that $N \ge 3$ (in comparison with S. Lee's approach, which is tailored for $N=2$).

So here we go:

Question 2. (baby version) What is the maximum value of the expectation $$ \mathbb E[|(X-Y)(Y-Z)(Z-X)|] = \int_{[0,1]^3} |(X-Y)(Y-Z)(Z-X)| d\mu(X) d\mu(Y)d\mu(Z) $$ among all probability measures $\mu$ on $[0,1]$?

  • 1
    $\begingroup$ The maximum is equal to the maximum of $|f|$ is exists: take $\mu_j$ to be point masses at $x_j$ where $x=(x_1,...,x_n)$ is the point of maximum of $|f|$. If $|f|$ has no maximum, then your expression also has no maximum. $\endgroup$ – Alexandre Eremenko Feb 15 at 13:19
  • 6
    $\begingroup$ @Alexandre This argument only works if the maximum is attained at a point which has all coordinates same because the points are i.i.d. $\endgroup$ – Ankitp Feb 15 at 13:31
  • 3
    $\begingroup$ Maybe I am mistaken, but here is another random thought. Because the expectation is symmetric, in fact it is the same to consider the expectation of $|f|$ or that of $x\mapsto\sum_\sigma |f(\sigma\cdot x)|/n!$, where $\sigma$ ranges over all permutations. So you need only consider symmetrical functions. $\endgroup$ – Pierre PC Feb 18 at 12:43
  • 2
    $\begingroup$ Actually that cubic is not so good, since the integral will be identically zero by symmetry. $\endgroup$ – Matt F. Feb 18 at 20:16
  • 3
    $\begingroup$ I might misunderstand the problem, but if you do not take the absolute value, for a polynomial of degree $n$ you have the expectation $$E\bigl[\sum_{|\alpha| < n} a_{\alpha} x^{\alpha}\bigr] = \sum_{|\alpha| < n} a_{\alpha} \, m_{\alpha_1} \cdots m_{\alpha_k}$$ in terms of the moments $m_i$, $0 \le i \le n$. So after you find the numbers $m_i$ that maximise the sum for the coefficients of the polynomial, you have a Hausdorff moment problem. $\endgroup$ – student Feb 18 at 21:05

We can in principle find the optimal first $n$ moments for a polynomial approximation of $f$. This reduces the problem to a truncated Hausdorff moment problem, for which solutions exist.

Let $f \colon [0,1]^d \to \mathbb{R}$ be a continuous function. Approximate $|f|$ with the multivariate Bernstein polynomials of order $n$ (writing $f_{k_1\cdots k_d} = f(k_1/n, \ldots k_d/n)$ for short),

$$|f_n|(x) = \sum_{k_1 \cdots k_d} |f_{k_1\cdots k_d}| \prod_{i=1}^{d} \binom{n}{k_i} \, x_i^{k_i} (1-x_i)^{n-k_i} \;.$$

Let $S_k = E[X^k (1-X)^{n-k}]$. The expectation of the approximant is then

$$E[|f_n|(X)] = \sum_{k_1 \cdots k_d} |f_{k_1\cdots k_d}| \prod_{i=1}^{d} \binom{n}{k_i} \, S_{k_i} \;,$$

which should be maximal. The value of $S_{k}$ is a linear combination on the first $n$ moments $M$ of the resulting distribution. Expanding

$$S_k = \sum_{r=0}^{n} \binom{n-k}{r-k} (-1)^{r-k} \, M_r \;,$$

the expectation in terms of the moments is

$$E[|f_n|(X)] = \sum_{k_1 \cdots k_d} \sum_{r_1 \cdots r_d} |f_{k_1\cdots k_d}| \prod_{i=1}^{d} \binom{n}{r_i} \binom{r_i}{k_i} (-1)^{r_i-k_i} \, M_{r_i} \;.$$

The problem is hence the constrained multilinear optimisation

$$ \begin{aligned} \max_{M_0 \cdots M_n} \;&\; \sum_{k_1 \cdots k_d} \sum_{r_1 \cdots r_d} |f_{k_1\cdots k_d}| \prod_{i=1}^{d} \binom{n}{r_i} \binom{r_i}{k_i} (-1)^{r_i-k_i} \, M_{r_i} \;, \\ \text{s.t.} \;&\; \sum_{r=0}^{n} \binom{n-k}{r-k} (-1)^{r-k} \, M_r \ge 0 \;, \\ &\; M_r \in [0,1] \;, \quad M_0 = 1 \;. \end{aligned} $$

To make the problem slightly more tractable, the relation between $S_k$ and $M_r$ can be inverted using the binomial partial sums,

$$M_r = \sum_{k=0}^{n} \binom{n-r}{k-r} \, S_k \;,$$

so that the optimisation becomes

$$ \begin{aligned} \max_{S_0 \cdots S_n} \;&\; \sum_{k_1 \cdots k_d} |f_{k_1\cdots k_d}| \prod_{i=1}^{d} \binom{n}{k_i} \, S_{k_i} \;, \\ \text{s.t.} \;&\; \sum_{k=0}^{n} \binom{n-r}{k-r} \, S_k \in [0,1] \;, \\ &\; \sum_{k=0}^{n} \binom{n}{k} \, S_k = 1 \;, \quad S_k \ge 0 \;. \end{aligned} $$

However, the first constraint is actually superfluous, as it is implied by the normalisation. Letting further $p_k = \binom{n}{k} S_k$, we have the optimisation problem in its most basic form,

$$ \begin{aligned} \max_{p_0 \cdots p_n} \;&\; \sum_{k_1 \cdots k_d} |f_{k_1\cdots k_d}| \prod_{i=1}^{d} p_{k_i} \;, \\ \text{s.t.} \;&\; \sum_{k=0}^{n} p_k = 1 \;, \quad p_k \ge 0 \;. \end{aligned} $$

which is simply the maximum expectation of the discretised function $|f_{k_1 \cdots k_d}|$ from the discrete distribution $p$.

From a cursory Google search, multilinear optimisation appears difficult (but I have no idea, really). Nevertheless, it is a recipe for calculations: For the baby problem, I find (with $n = 50$) the expectation 0.0615760 and distribution $p$ as below.

discrete distribution

  • $\begingroup$ For the distribution in my answer, I found moments of {1/2, 37/90, 11/30, 113/330, 65/198, 457/1430, 449/1430, 121/390, 4/13, 26/85, 233/765, 1471/4845}, which are higher than your moments by {0, 0.011, 0.016, 0.019, 0.020, 0.021, 0.022, 0.022, 0.022, 0.023, 0.023, 0.024}. $\endgroup$ – Matt F. Feb 23 at 21:34
  • 1
    $\begingroup$ For the moments of the optimisation, I get an expectation of 0.061419, which is slightly higher than 503582/8204625. $\endgroup$ – student Feb 23 at 21:41
  • $\begingroup$ What about the polynomial integrand $(X-Y)^2(Y-Z)^2(X-Z)^2$? (Baby squared) My experiments indicate that in this case the maximum is attained at the discrete measure equidistributed at $0$, $1/2$, $1$. $\endgroup$ – Jairo Bochi Feb 25 at 7:41
  • 1
    $\begingroup$ @JairoBochi Same, with max expectation $\sim 0.0138889$. Same for powers of three. I wonder what the appearance of a continuous component depends on. $\endgroup$ – student Feb 25 at 8:13

Disclaimer: This is a bit too much "abstract nonsense" for me to really consider it a satisfying answer to the question, but on the other hand the question is so general that I'm not sure how much better one can do.

Let's write $E=[0,1]$ and $P(E)$ for probability measures on $E$. Let $f : E^n \to \mathbb{R}$ be bounded and measurable. (Actually, this all holds for an arbitrary Polish space $E$.) I claim that you can write

$$\sup_{\mu \in P(E)}\int_{E^n} f\,d\mu^n = \lim_{k\to\infty}\sup_{(x_1,\ldots,x_k) \in E^k}\frac{1}{k!}\sum_{\sigma \in S_k}f(x_{\sigma(1)},\ldots,x_{\sigma(n)}),$$

where $S_k$ is the set of permutations of $\{1,\ldots,k\}$, and $\mu^n$ is the $n$-fold product measure. As a sanity check, note that if $n=1$ then you can take $x_1=\ldots=x_k$ in the supremum to get $\sup f$. Not sure what to do with this for $n\ge 2$.

Proof: Fix $k \ge n$ for now. Then

$$\int_{E^n}f\,d\mu^n = \int_{E^k}f(x_1,\ldots,x_n)\,\mu^k(d(x_1,\ldots,x_k))$$

is a linear functional of $\mu^k$. The supremum of a linear functional over a convex set is the same as over its closed convex hull. Thus, if $C_k \subset P(E^k)$ denotes the closed (in total variation) convex hull of $\{\mu^k : \mu \in P(E)\}$, then

$$\sup_{\mu \in P(E)}\int_{E^n}f\,d\mu^n = \sup_{\nu \in C_k}\int_{E^k}f(x_1,\ldots,x_n)\,\nu(d(x_1,\ldots,x_k)).$$

Note that $C_k$ consists of all "mixtures of iid". Because De Finetti's theorem fails for finite exchangeable sequences, these "mixtures of iid" are not quite all exchangeable probability measures on $E^k$. However, if $P_e(E^k)$ denotes the set of exchangeable (invariant under permutations) probability measures on $E^k$, then a theorem of Diaconis-Freedman (Theorem 13 here) says that for each $\eta \in P_e(E^k)$ there exists $\nu \in C_k$ such that the projections $\eta|_n$ and $\nu|_n$ onto the first $n$ coordinates satisfy $\|\eta|_n-\nu|_n\|_{TV} \le n(n-1)/2k$, with $\|\cdot\|_{TV}$ denoting total variation. It follows that

$$\left|\sup_{\mu \in P(E)}\int_{E^n}f\,d\mu^n - \sup_{\eta \in P_e(E^k)}\int_{E^k}f(x_1,\ldots,x_n)\,\eta(d(x_1,\ldots,x_k))\right| \le \|f\|_\infty n(n-1)/2k.$$

Next, for a given $\eta$ we can average over all permutations of coordinates in $f$ without changing the value of the integral. That is,

$$\sup_{\eta \in P_e(E^k)}\int_{E^k}f(x_1,\ldots,x_n)\,\eta(d(x_1,\ldots,x_k)) = \sup_{\eta \in P_e(E^k)}\int_{E^k}\hat{f}_k\,d\eta,$$

where the function $\hat{f}_k : E^k \to \mathbb{R}$ is defined by

$$\hat{f}_k(x_1,\ldots,x_k) = \frac{1}{k!}\sum_{\sigma \in S_k}f(x_{\sigma(1)},\ldots,x_{\sigma(n)}).$$

We can then "un-symmetrize" the measures $\eta \in P_e(E^k)$ to reach a supremum over all probabilities on $E^k$:

$$\sup_{\eta \in P_e(E^k)}\int_{E^k}\hat{f}_k\,d\eta = \sup_{\eta \in P(E^k)}\int_{E^k}\hat{f}_k\,d\eta.$$

The right-hand side is simply $\sup \hat{f}_k$. Putting it all together gives

$$\sup_{\mu \in P(E)}\int_{E^n}f\,d\mu^n = \lim_{k\to\infty}\sup_{\eta \in P_e(E^k)}\int_{E^k}f(x_1,\ldots,x_n)\,\eta(d(x_1,\ldots,x_k)) = \lim_{k\to\infty}\sup \hat{f}_k,$$

which was the original claim above.

  • $\begingroup$ Gosh, thanks a lot for such an answer! I am very happy to read your contribution. I am not an expert in probability (I am not familiar with De Finetti or Diaconis-Friedman theorem) so I need sometime to study and understand your approach, which looks interesting. As a "concrete" example, may I ask you to kindly give a look here (where actually the whole story began)? Probably in that specific example your approach can yield the final answer... Thanks again! $\endgroup$ – Romeo Feb 22 at 16:38
  • $\begingroup$ '...says that for each $\eta \in P_e(E^k)$ there exists $\nu \in C_k$ ...' For your argument to work, shouldn't it be the other way around: for each $\nu \in C_k$ there exists $\eta \in P_e(E^k)$, and so on? $\endgroup$ – Sinusx Feb 22 at 23:38
  • $\begingroup$ @Sinusx The other way around is immediate because $C_k \subset P_e(E^k)$. $\endgroup$ – Dan Feb 24 at 16:10
  • $\begingroup$ @Romeo The more I reflect on this answer the more useless it seems. Very different things can happen in different examples, so you should not expect a very general answer. An important example from random matrix theory involves the "non-commutative entropy": Take $n=2$ and $f(x,y) = \log|x-y| - x^2 - y^2$. Then, over all probability measures $\mu$ on $[-1,1]$, the value of $\int_{[-1,1]^2}f(x,y)\mu(dx)\mu(dy)$ is maximized by the semicircle law $\mu(dx) = \frac{2}{\pi}\sqrt{1-x^2}dx$. See e.g. Theorem 1.3 here: link.springer.com/article/10.1007/s004400050119 $\endgroup$ – Dan Feb 24 at 16:12
  • 1
    $\begingroup$ I am trying to understand this result. We can write $f(x_{\sigma(1)} \ldots x_{\sigma(n)})$ as an integral over $f$ and delta distributions at $x_{\sigma(1)} \ldots x_{\sigma(n)}$, $$\int \! f(x_1' \ldots x_n') \, \Bigl\{\frac{1}{k!}\sum_{\sigma \in S_k} \delta(x_1' - x_{\sigma(1)}) \cdots \delta(x_n' - x_{\sigma(n)}) \Bigr\} \, d^nx' \;.$$ So has this essentially shown that there is a maximising distribution, and that it can be approximated by sufficiently many point masses? $\endgroup$ – student Feb 25 at 14:53

Suppose $\mu = \mu_{\max}$ is a (Borel) probability measure on $[0,1]$ for which the following integral is maximized: \begin{equation}\tag{1} \mathcal{I}(\mu) := \iiint |(y-x)(z-y)(z-x)| d\mu(x)d\mu(y)d\mu(z) \end{equation} Existence of $\mu_{\max}$ is relatively easy to prove. We will prove the following

Proposition: \begin{equation}\tag{2}\label{infinite} \limsup_{\epsilon \to 0} \frac{\mu_{\max}([0,\epsilon])}{\epsilon} = + \infty. \end{equation} That is, $\mu_{\max}$ has infinite density at $0$ and (by symmetry) also at $1$.

Here are some open questions:

  • Is $\mu_{\max}$ atomic at $0$ and $1$?
  • Is $\mu_{\max}$ absolutely continuous elsewhere?

First, we prove a

Lemma: If $X$ is a random variable with measure $d\mathbb{P}$ and cdf $F(x)$ satisfying $F(x)\le cx$ on $x\in [0,1]$, and $p=F(\epsilon)$, then

\begin{equation} \int_{[X \le \epsilon]} X d\mathbb{P} \ge \frac{p^2}{2c} \, . \end{equation}

Proof of Lemma: By a variant of the well-known identity for a non-negative random variable $X$: \begin{equation} \mathbb{E}(X) = \int_0^\infty (1-F(x))dx, \end{equation} we have \begin{align} \int_{[X\le \epsilon]} X d\mathbb{P} &= \int_0^\epsilon (p-F(x)) dx \\ &\ge \int_0^\epsilon \max(p-cx,0) dx \\ &\ge \int_0^{p/c} (p-cx) dx \\ &= \frac{p^2}{2c}. \end{align} This proves the Lemma.

The proof of the Proposition \eqref{infinite} is by contradiction. Let $\mu=\mu_{\max}$ and let $F(x) := \mu([0,x])$ be the cumulative distribution function. If the proposition does not hold, then there exists a finite $c \ge 1$ such that, for all $x\in [0,1]$, \begin{equation}\tag{3}\label{linear} F(x) \le cx . \end{equation} Note that $F(x)>0$ for every $x>0$, because if $F(x)=0$ for some $x>0$ then we can increase $I$ by replacing $\mu$ with $h_*\mu$, where $h$ is the affine map that sends $[x,1]$ onto $[0,1]$.

Let $X$, $Y$, $Z$ be independent random variables distributed according to $\mu$; let $\mathbb{P}$ denote the underlying probability. Fix a constant $\gamma \in (0,1)$ such that the following event $\Gamma$ has probability at least $1/2$: \begin{equation}\tag{4}\label{Gamma} \Gamma := \big[ Y>\gamma, \ Z>\gamma, \text{ and } |Y-Z|>\gamma \big]. \end{equation} Consider random variables $X_1 \le X_2 \le X_3$ obtained by ordering $X$, $Y$, $Z$; so $X_1 = \min(X,Y,Z)$, for instance. Fix a number $\epsilon$ in the range \begin{equation}\tag{5}\label{epsilon} 0 < \epsilon < \frac{\gamma^2}{12c}. \end{equation} Define random variables $\tilde{X}_1 \le \tilde{X}_2 \le \tilde{X}_3$ by: \begin{equation}\tag{6} \tilde X_i := \begin{cases} X_i &\text{if } X_i > \epsilon, \\ 0 &\text{otherwise.} \end{cases} \end{equation} Finally, define \begin{align} \Pi &:= (X_2-X_1)(X_3-X_1)(X_3-X_1) , \\ \tilde \Pi &:= (\tilde X_2- \tilde X_1)(\tilde X_3- \tilde X_1)(\tilde X_3- \tilde X_1) . \tag{7} \end{align} We claim that: \begin{equation}\tag{8}\label{mainclaim} \mathbb{E}(\Pi) < \mathbb{E}(\tilde \Pi) . \end{equation} This means that $\mathcal{I}(\mu) < \mathcal{I}(\nu)$, where $\nu$ is the probability measure obtained from $\mu$ by crunching all the mass of the interval $[0,\epsilon]$ on the point $0$. So the claim \eqref{mainclaim} implies that $\mu$ is not a maximizer, and therefore it is sufficient to prove it in order to conclude \eqref{infinite}.

Note that $\tilde \Pi = \Pi$ whenever $X_1 > \epsilon$, and so: \begin{equation}\tag{9} \mathbb{E}(\tilde \Pi - \Pi) = \int_{[X_1 \le \epsilon]} (\tilde \Pi - \Pi) d\mathbb{P} . \end{equation} On the other hand, $X_2 \le \epsilon$ implies $\tilde \Pi = 0$, so: \begin{equation}\tag{10}\label{difference} \mathbb{E}(\tilde \Pi - \Pi) = - \int_{[X_2 \le \epsilon]} \Pi d\mathbb{P} + \int_{[X_1 \le \epsilon < X_2]} (\tilde \Pi - \Pi) d\mathbb{P} =: -I_1 + I_2 . \end{equation} Let us estimate the integrals $I_1$ and $I_2$.

Let \begin{equation}\tag{11}\label{p} p := \mu([0,\epsilon]) = \mathbb{P}[X\le \epsilon]. \end{equation} Then: \begin{align} \mathbb{P}[X_2\le \epsilon] &= \mathbb{P}[X_2\le \epsilon < X_3] + \mathbb{P}[X_3\le \epsilon] \\ &= 3p^2(1-p) + p^3 \\ &\le 3p^2 \, . \tag{12} \end{align} On $[X_2 \le \epsilon]$ we have $\Pi \le X_2-X_1 \le X_2 \le \epsilon$. Therefore we obtain the bound \begin{equation}\tag{13}\label{I1} I_1 \le 3 \epsilon p^2 . \end{equation}

Let us estimate $I_2 = \int_{[X_1 \le \epsilon < X_2]} (\tilde \Pi - \Pi) d\mathbb{P}$ from below. Note that the integrand is non-negative, and so \begin{equation}\tag{14} I_2 \ge \int_{G} (\tilde \Pi - \Pi) d\mathbb{P} \end{equation} for any measurable set $G \subseteq [X_1 \le \epsilon < X_2]$. We choose $G:= [X \le \epsilon] \cap \Gamma$, where $\Gamma$ is as in \eqref{Gamma}. Over $G$ we have: \begin{align} \tilde \Pi - \Pi &= YZ |Y-Z| - (Y-X) (Z-X) |Y-Z| \\ &= X(Y + Z - X) |Y-Z| \\ &\ge \gamma^2 X.\tag{15} \end{align} Therefore: \begin{equation}\tag{16} I_2 \ge \int_{G} (\tilde \Pi - \Pi) d\mathbb{P} \ge \gamma^2 \int_{[X \le \epsilon] \cap \Gamma} X d\mathbb{P} = \gamma^2 \mathbb{P}(\Gamma) \int_{[X \le \epsilon]} X d\mathbb{P} \end{equation} (since the random variable $X$ is independent from the event $\Gamma$).

Then by the lemma, $I_2 \ge \gamma^2 p^2/4c$ and therefore: \begin{equation}\tag{17} \mathbb{E}(\tilde \Pi - \Pi) = I_2 - I_1 \ge \left(\frac{\gamma^2}{4c} - 3 \epsilon \right) p^2 > 0, \end{equation} by our choice of $\epsilon$. This proves the claim \eqref{mainclaim} and the Proposition.

  • 1
    $\begingroup$ I think an interesting question here is what property i) makes this $f$ (and its square and cube) have discontinuous parts, ii) makes $f$ have also a continuous part, but iii) makes e.g. $f = xyz$ have no point mass (but what seems like a funky distribution)? Edit: Actually I think it is not even clear that these are discontinuities. $\endgroup$ – student Feb 25 at 17:29
  • $\begingroup$ FWIW the family of distributions I was thinking about is something like $\bigl(x^2 (1-x)^2 (1-2x)^2\bigr)^{-t}$ near $t=1/2$. $\endgroup$ – student Feb 26 at 16:04
  • 1
    $\begingroup$ Another open question: does every subinterval of [0,1] have positive probability? $\endgroup$ – Matt F. Feb 26 at 20:22

We can attack the baby problem directly, as Jairo Bochi and I did jointly in the comments.

For instance, the distribution which is a mixture of:

  • $3/5$ of a Bernoulli distribution with $p=1/2$
  • $2/5$ of a Beta distribution with $\alpha=\beta=4$

yields an expectation equal to $503582/8204625$, or approximately $.0614$. This mixture of a discrete and a continuous distribution seems to be almost optimal.

  • $\begingroup$ Nice! This agrees much more with my experiments. As you say, your distribution seems almost optimal, but not exactly optimal. Indeed, the optimal distribution supported on a uniform mesh of 20 points yields an integral >.0615. $\endgroup$ – Jairo Bochi Feb 23 at 7:05
  • $\begingroup$ @JairoBochi, I figure what it lacks in optimality, it makes up for with round parameters and being exactly calculable. $\endgroup$ – Matt F. Feb 23 at 7:07
  • $\begingroup$ I see. But is there any reason to believe that the true optimizer will have a Beta component? $\endgroup$ – Jairo Bochi Feb 23 at 7:09
  • 2
    $\begingroup$ Since it seems out of reach to find an exact solution for the baby problem (unless a miracle happens), I think that a more modest but still very nice problem would be to prove that any (the unique?) optimizing distribution is indeed a non-trivial mixture of discrete and continuous distributions. $\endgroup$ – Jairo Bochi Feb 23 at 7:11
  • $\begingroup$ The symmetric Beta distribution is one of the simplest distributions on the unit interval. Perhaps it maximizes a function of the order statistics like $E[X_{(2)}-X_{(1)}]/\sigma$; I'd be surprised if it's not the solution to some optimization. $\endgroup$ – Matt F. Feb 23 at 7:21

The general problem appears to depend on the properties of moments of measures supported on the unit interval. Shohat and Tamarkin is a somewhat standard reference for classical moment problems.

For the specific problem, here is a plot of a numerically constructed maximizing measure on 50 point masses of equal weights (the optimal value as noted in the comments above is at least $.0615$, and does not appear to agree with the smaller value of $.0614$ that is circulating). The measure is symmetric:

enter image description here

Also here is the companion python code which should be optimized if the number of points is substantially increased.

    % Code below uses pymanopt manifold optimization package
    import autograd.numpy as np
    import numpy

    from pymanopt import Problem
    from pymanopt.manifolds import Sphere, Product, Oblique
    from pymanopt.solvers import ConjugateGradient

    def cost2(M,N,P):
      W =  np.abs((M-N)*(N-P)*(P-M))
      return W

    def emax():
      manifold = Oblique(2,50)
      solver = ConjugateGradient(mingradnorm=8e-12, minstepsize=1e-9,maxiter=35)
      def cost(X):
       Z = X*X
       a = np.array([[[cost2(Z[0,i],Z[0,j],Z[0,k]) for i in range(50)] for j in range(50)] for k in range(50)])
       S = -sum(sum(sum(a)))
       return S
      return solver.solve(problem)


One can quickly adjust this for non-evenly weighted points by changing a couple of lines:

manifold = Product([Oblique(2,50),Sphere(50)])...
Z = X[0]*X[0]...
Z1 = X[1]*X[1]...
a = np.array([[[cost2(Z[0,i],Z[0,j],Z[0,k])*Z1[i]*Z1[j]*Z1[k] for i in range(50)] for j in range(50)] for k in range(50)])...

(which may or may not give a larger value, but should ideally).

Edit: Here is a (rough) numerical optimizer obtained by allowing weights. In this case the value $.06157$ is at least attainable using $50$ points.

enter image description here

Lastly, here is the part given by removing (what appears to be) delta masses at zero and one:

enter image description here

  • $\begingroup$ Thanks for the graph; this agrees with @JairoBochi's number in the comment to my answer. $\endgroup$ – Matt F. Feb 24 at 5:26
  • $\begingroup$ @MattF. The plot above is really a bit crude. It will be interesting to see what comes out of looking at approximations without equal mass. It would also be interesting to pin down the minimizer exactly, however it does not seem that that combination of Bernoullli and the Beta distribution is optimal. $\endgroup$ – Josiah Park Feb 24 at 5:33
  • 1
    $\begingroup$ I made no claim that the combination of Bernoulli and Beta distributions was optimal -- see the first two comments on my answer. $\endgroup$ – Matt F. Feb 24 at 6:03
  • $\begingroup$ In your first (fixed-weight) approach, the answer I suggested corresponds to 15 points at 0, 15 points at 1, and 20 points at the 2.5, 7.5, ... 97.5 percentiles of the Beta(4,4) distribution. $\endgroup$ – Matt F. Feb 24 at 11:37
  • $\begingroup$ Isn't it more efficient to fix the position of the masses and optimize for their weights? $\endgroup$ – Jairo Bochi Feb 24 at 13:31

Here is a complete and elementary solution for the Baby-squared problem, that is, the Baby Problem with the integrand squared. The idea is to reformulate the problem in terms of moments, and originates from @student's answer. By a linear change of coordinates, we can work on the interval $[-1,1]$ instead. The result becomes:

Proposition: For any Borel probability measure on $[-1,1]$, we have \begin{equation}\tag{1}\label{prop} \iiint (y-x)^2 (z-y)^2 (z-x)^2 \, d\mu(x)d\mu(y)d\mu(z) \le \frac{8}{9}. \end{equation} Equality holds if and only if \begin{equation}\tag{2}\label{three} \mu = \frac{\delta_{-1}+\delta_0+\delta_1}{3} . \end{equation}

Remark: As explained in other answers and comments, the maximizer $\mu$ for the original (un-squared) Baby Problem is certainly more complicated than \eqref{three}, and it seems to have a discrete and a continuous component.

Lemma: If $X$ is any random variable on $[0,1]$, then: \begin{equation}\tag{3} \mathbb{E}(X) \, \mathrm{Var}(X) \le \frac{4}{27} . \end{equation} Equality holds if and only if $X$ is Bernoulli with $\mathbb{P}[X=0] = 1/3$, $\mathbb{P}[X=1] = 2/3$.

Proof of the lemma: There should be a direct argument, but here is a somewhat informal proof. If $0<X<1$ with positive probability then we can deform the distribution of $X$ so that expectancy doesn't change and the variance increases. Therefore expectancy times variance can only be maximized if $X$ is Bernoulli taking values $0$ or $1$. Then the lemma becomes a Calculus exercise.

Proof of the proposition: Let $\mu$ be a Borel probability measure on $[-1,1]$. For each positive integer $n$, let $M_n := \int x^n \, d\mu(x)$ denote the $n$-th moment of $\mu$. Let $I$ denote the LHS of \eqref{prop}. Computation shows that: \begin{equation}\tag{4}\label{formula} I = 12 M_1 M_2 M_3 - 6 M_1^2 M_4 - 6 M_3^2 + 6 M_2 M_4 - 6 M_2^3. \end{equation} [Curiosity: the fact that the expression on the RHS is nonnegative is a well-known general property of moments; actually the RHS is $6$ times the determinant of the positive-semidefinite Hankel quadratic form $Q(u_0,u_1,u_2) := \int (u_0+u_1 x + u_2 x^2)^2 \, d\mu(x)$.] Note that: \begin{equation}\tag{5} 2 M_1 M_2 M_3 \le M_1^2 M_2^2 + M_3^2 \le M_1^2 M_4 + M_3^2 . \end{equation} This gives: \begin{equation}\tag{6}\label{nice} I \le 6 M_2 M_4 - 6 M_2^3. \end{equation}

Consider the probability measure $\hat{\mu}:=(\mu+r_* \mu)/2$ on $[-1,1]$, where $r(x) := -x$; this measure is symmetric around $0$ (in the sense that $r_* \hat{\mu} = \hat{\mu}$). The moments of $\hat{\mu}$ are: \begin{equation}\tag{7} \hat{M}_n = \begin{cases} M_n &\text{if $n$ is even,}\\ 0 &\text{if $n$ is odd.} \end{cases} \end{equation} Let $\hat{I}$ denotes integral on the LHS of \eqref{prop} with $\hat{\mu}$ in the place of $\mu$. Then inequality \eqref{nice} can be rewritten as: \begin{equation}\tag{8} I \le \hat{I} \, . \end{equation} Furthermore, it's easy to check that if $I = \hat{I}$ then $\mu$ is symmetric around $0$ or $I=0$.

Therefore we can assume that $\mu = \hat{\mu}$, that is, $\mu$ is symmetric around $0$. In this case, \begin{equation}\tag{9} I = 6 M_2 M_4 - 6 M_2^3 = 6 M_2 (M_4 - M_2^2) = 6 \mathbb{E}(X^2) \mathrm{Var}(X^2) \, , \end{equation} where $X$ is a random variable with distribution $\mu$. By the lemma, $I \le 8/9$, with equality if and only if $X^2$ is Bernoulli with $\mathbb{P}[X^2=0] = 1/3$ and $\mathbb{P}[X^2=1] = 2/3$. By symmetry, this means that $X$ is Bernoulli equidistributed on $\{-1,0,1\}$. The proposition is proved.

  • $\begingroup$ I realized that for any $t \geq 2$, the integral $\iiint_{[-1,1|^3} |(x-y)(y-z)(x-z)|^t$ is maximized by the same measure $(\delta_{-1}+\delta_0+\delta_1)/3$. This follows from the fact that the integrand is maximized at the point $(-1,0,1)$. $\endgroup$ – Jairo Bochi Mar 1 at 17:52

Your Answer

By clicking "Post Your Answer", you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.