# Why is the Barycenter operation in Hadamard spaces Lipschitz continuous?

I am looking into exercise 9.2.22 of "A course in metric geometry" by Burago-Burago-Ivanov.

For a Hadamard space $H$ (a complete simply connected metric space of nonpositive curvature in the Alexandrov sense) and a fixed point $p \in H$ the function $f(x) = (d(p,x))^2$ is strongly convex, where $d$ denotes the metric of $H$.

Therefore for a fixed collection of points $p_1, \ldots, p_n$ the function $$f(x) = \sum_{i=1}^n (d(p_i,x))^2$$ is also strictly convex and therefore has a unique point $y$ where $f$ attains its maximum. The Point $y$ is called the Barycenter of $p_1, \ldots ,p_n$.

The Statement I want to prove is the following:

The Barycenter operation is Lipschitz continuous, i.e if $q_1, \ldots q_n$ is another collection of points and $y'$ is their barycenter then one has a relation of the form $$d(y,y') \leq L \cdot \left( d(p_1,q_1) + \ldots + d(p_n,q_n) \right)$$

I suspect that i need some statement of the form: If strongly convex functions $f,g$ satisfy $\Vert f-g\Vert_{\infty}< C$ then their Minima $y,y'$ satisfy $d(y,y')< CL$. Does such a statement exist?

• Which question are you actually asking: Solution of the exercise or the more general conjecture about convex functions? – Moishe Kohan May 3 '16 at 23:59
• I am asking for the solution to the exercise. I am thinking that it has something to do with the 2-convexity of the distance squared functions. I would also be happy with a solution, which does not utilize the convexity of the functions, if such does exist. – Near May 4 '16 at 11:42
• OK, I will then write down my solution. It proves more (about a general class of convex functions), but not as much as your conjecture, which, I think, needs some further assumptions, something like "uniform strict convexity" for the functions you are considering. – Moishe Kohan May 4 '16 at 14:46
• I agree. The answer which was deleted here showed, that there is not way for "just" strongly convex functions to get a statement I proposed in my question. However the functions in question are not "just" strongly convex but 2-convex. Meaning that the eigenvalues of the hessian are bounded below by 2, if the functions are differentiable. – Near May 4 '16 at 14:54
• @studiosus Strong convexity is "uniform strict convexity". – user147263 May 4 '16 at 22:59

Here is a proof in the setting of complete simply connected Riemannian manifolds of nonpositive sectional curvature. The case of general CAT(0) spaces should be similar, but there are some technical details which make the discussion less pleasant in that case: One needs to smooth out convex functions of one variable (which can be done via convolution with standard mollifiers), one also has to address the issue that geodesics in general CAT(0) spaces are not extendible as well as discuss the 1-st and 2nd variation formulae for the lengths of geodesics in terms of Alexandrov angles (instead of Riemannian ones). Since the solution is already much longer than I would have liked, I will not pursue the general CAT(0) case.

Another remark is that the same proof with very minor modifications goes through if one puts some fixed probability measures on the finite sets in question and considers barycenters of these measures.

Thus, let $M$ be a Riemannian manifold as above, $P=(p_1,...,p_n)$, $Q=(q_1,...q_n)$. Let $b_P, b_Q$ denote their respective barycenters. The first thing to note is that if we rescale the metric on $M$, the locations of the barycenters do not change and the nonpositive curvature condition is also retained. Furthermore, the Lipschitz constant of the barycenter map will not change. Therefore, after rescaling, we can (and will) assume that $$\max_{i\ne j} d(p_i, p_j)= d(p_1, p_2)=1.$$ The barycenter $b_P$ of $P$ lies in the convex hull of $P$ (which has diameter $1$), hence, its distance to all the points $p_i$ is at most $1$. Since the Lipschitz property is a local condition, it suffices to consider the case when $$\max_{i} d(p_i,q_i)$$ is small, say, $\le \frac{1}{10}$. Define convex functions $\Phi_i(x):= d(p_i, x)$, $\Psi_i(x)= d(q_i,x)$, $i=1,...,n$ on $M$ and $$F(x)= \sum_i \Phi^2_i(x), G(x)= \sum_i \Psi^2_i(x).$$ We consider the following interpolation between the functions $F$ and $G$: $$H(x,t)= \sum_i ( (1-t) \Phi_i + t \Psi_i)^2.$$

Note. When I first tried to use the convex interpolation between $F$ and $G$, the argument did not work.

Consider the complete geodesic $\gamma$ in $M$ passing through the barycenters of $P$ and $Q$ respectively and restrict $\Phi_i$'s, $\Psi_i$'s and $H$ to this geodesic. The restriction of $H(\cdot, t)$ is proper strictly convex function which I will denote $h(s,t)$. For each $t$ let $s_t$ denote the point of unique minimum of $h(s,t)$ on ${\mathbb R}$; then $\gamma(s_0)$ and $\gamma(s_1)$ are the barycenters of $F$ and $G$ respectively. Let $s_0$ and $s_1\in {\mathbb R}$ be such that $\gamma(s_0)=b_P$, $\gamma(s_1)=b_Q$.

I will get uniform Lipschitz bound on the function $t\mapsto s_t, 0\le t\le 1$, and use it to get a uniform Lipschitz bound on the function $$(P,Q)\mapsto d(b_P, b_Q).$$ (Of course, the global minumum of $H(t, \cdot)$ need not lie on the geodesic $\gamma$ for $t\ne 0, 1$.)

Remark. There is a slightly unpleasant technical issue here, namely that $h(s,t)$ is smooth except, possibly, on a finite union of lines in ${\mathbb R}^2$, this lack of smoothness happens when one of the points $p_i, q_j$ lies on the geodesic $\gamma$. We will address this issue by getting a uniform bound away from these lines and integrating on the interval $0\le t\le 1$.

Working away from these lines, we now have the smooth function $h(s,t)$. Its point of minimum $s_t$ is given by the equation $$\frac{\partial h}{\partial s}(s_t)=0.$$ By the implicit function theorem, we obtain $$|\frac{ds_t}{t}|= |\frac{\partial Dh}{\partial t}(s_t)| \cdot |\frac{\partial D^2h}{\partial s^2}(s_t)|^{-1}$$ Thus, our goal is to bound $|\frac{\partial Dh}{\partial t}(s_t)|$ from above and $\frac{\partial D^2h}{\partial s^2}(s_t)$ from below, away from $0$ (it is already positive by convexity, the issue is a uniform positive lower bound). Note that since $b_P, b_Q$ and, hence, the geodesic segment $b_Pb_Q$ are contained in the union of the convex hulls of $P$ and $Q$ and which has diameter $\le 2$, it follows that

1. An upper bound for the numerator: $$\frac{\partial Dh}{\partial t} = 2 \sum_i ((1-t)\phi_i(s) + t \psi_i(s))(\psi_i(s) - \phi_i(s)).$$ Here and below, $\phi_i(s)= \Phi_i(\gamma(s))$, $\psi_i(s)= \Psi_i(\gamma(s))$. Since the distance functions are $1$-Lipschitz, we obtain: $$|\psi_i(s) - \phi_i(s)|\le d(p_i,q_i), i=1,...,n.$$ On the other hand, since $b_Pb_Q$ is contained in a subset of diameter $\le 2$ in $M$, we obtain $$\phi_i(s)\le 2, \psi_i(s)\le 2$$ for all $s\in [s_0, s_1]$. Hence, for $s\in [s_0, s_1]$, $$|\frac{\partial h}{\partial t}|\le 2n \max_i d(p_i,q_i).$$

2. Lower bound for the denominator. We have: $$\frac{\partial D^2h}{\partial s^2}= 2\sum_i [ ((1-t)\phi_i' + t \psi_i'))^2 + ((1-t)\phi_i + t \psi_i)) ((1-t)\phi_i'' + t \psi_i''))$$ Each $i$-th term of this sum is $\ge 0$, thus, it suffices to bound the sum of the first two terms ($i=1, 2$) from below. By the triangle inequality, for each $s$, $$d(\gamma(s), p_1) + d(\gamma(s), p_2)\ge d(p_1, p_2)=1$$ and $$d(\gamma(s), q_1) + d(\gamma(s), q_2) \ge 0.8,$$ (since we assumed that $\max_i d(p_i, q_i)\le 0.1$). Therefore, it suffices to consider those values of $s$ for which $d(\gamma(s), p_1)\ge 0.5$ and $d(\gamma(s), q_1)\ge 0.4$ and bound below the first term ($i=1$), otherwise, we will be bounding the second term.

To estimate the terms $\phi_1'(s)$ and $\psi_1'(s)$ we will use the 1st variation formula. Let $\alpha=\alpha_1(s)$ denote the angle $\angle p_1 \gamma(s) b_P$; let $\beta=\beta_1(s)$ denote the angle $\angle q_1 \gamma(s) b_P$. The first variation formula for the length of the segment $p_1\gamma(s)$ then reads $$\phi_1'(s)= \frac{1}{2} \cos(\alpha)/d(p_1,\gamma(s))= \frac{1}{2} \cos(\alpha)/\phi_1(s)$$ and, similarly, $$\psi_1'(s)= \frac{1}{2} \cos(\beta)/\psi_1(s).$$ We also have $\phi_1(s), \psi_1(s)\in [0,2]$. Since $d(p_1,q_1)\le 0.1$ and $d(\gamma(s), p_1)\ge 0.5, d(\gamma(s), q_1)\ge 0.4$, we obtain a bound on the difference $|\alpha-\beta|$: $$|\sin((\alpha- \beta)/2)|\le 1/8,$$ $$|\alpha- \beta|\le 0.06 \approx \pi/25.$$ Thus, as long as, say, $\alpha\in [0, \pi/6]\cup [\pi- \pi/6, \pi]$, we obtain the uniform lower bound $$((1-t)\phi_1' + t \psi_1'))^2 \ge \frac{1}{4}>0.$$

Thus, consider the case when $$\alpha\in [\pi/6, \pi- \pi/6], \beta\in [\pi/12, \pi- \pi/12].$$ Consider the term $$((1-t)\phi_1 + t \psi_1)) ((1-t)\phi_1'' + t \psi_1'').$$ We have: $$\phi_1(s)\ge 0.5, \psi_1(s)\ge 0.4$$ and $$(1-t)\phi_1(s) + t \psi_1(s)\ge 0.4.$$ It remains to bound $((1-t)\phi_1'' + t \psi_1'')$. Taking into account that $M$ has nonpositive sectional curvature, we obtain: $$\phi_1''(s)\ge \frac{\sin^2(\alpha)}{d(p_1, \gamma(s))} \ge 0.5 \sin^2(\alpha) \ge \frac{1}{8}$$ and $$\phi_1''(s)\ge \frac{\sin^2(\beta)}{d(q_1, \gamma(s))} \ge 0.5 \sin^2(\beta) \ge \frac{1}{32}.$$ Thus, $$((1-t)\phi_1 + t \psi_1)) ((1-t)\phi_1'' + t \psi_1'') \ge \frac{0.4}{32}.$$

In either case, we obtain a uniform upper bound
$$|\frac{ds_t}{t}| \le 80n \max_i d(p_i,q_i)$$ By integration on $[0,1]$, we obtain: $$d(b_P, b_Q)= |s_1 - s_0|\le 80n \max_i d(p_i,q_i)$$ This is the required Lipschitz property of barycenters. qed

Edit. For anybody who might still care (and for myself if I cannot find this reference in the future):

K-T.Sturm proved a general (and much sharper) result about Lipschitz property of barycenter map for probability measures on $CAT(0)$ spaces $X$:

Theorem. (Theorem 6.3 in [1]) Let $B(\mu)$ denote the barycenter of a probability measure $\mu$ on $X$. Then $$d(B(\mu_1), B(\mu_2))\le d^{W}(\mu_1, \mu_2)$$ where $d^W$ is the Wasserstein distance between the measures.

[1] K.-T. Sturm. Probability measures on metric spaces of nonpositive curvature. In Heat kernels and analysis on manifolds, graphs, and metric spaces. Contemp. Math. 338 (2003), 357-390

• Thanks for working this out in detail, but I am now puzzled by the authors' decision to make this an exercise in their book... – user147263 May 7 '16 at 19:55
• @sandwich: My solution is not necessarily optimal, there might be a simpler argument. You may ask the authors directly what (if anything) did they have in mind. – Moishe Kohan May 8 '16 at 2:22

(1) In a Hardmard space $(X ,|\ |)$, define a convex set $K$ wrt $|\ |$ Then then we have $$f : X\rightarrow \mathbb{R},\ f(x)=|xK|\ ({\rm or}\ |xK|^2)$$ Note that $f$ attains a minimum at $x_0$ and $x_0$ is unique So we have $$\pi : X\rightarrow K,\ \pi (x)=x_0$$

Then $\pi$ is distance nonexpanding

Proof : Note that $$\angle (xx_0,y_0x_0),\ \angle (yy_0,x_0y_0) > \frac{\pi}{2}$$

If ${\rm geod}_{[ab]}(t)$ is unit speed geodesic from $a$ to $b$, then we have first variation formula, since $X$ is Hadamard space, $$|{\rm geod}_{[x_0x]} (t)- {\rm geod}_{[y_0y]} (\tau ) | = |x_0y_0| -t\cos\ \angle (xx_0,y_0x_0) -\tau \cos\ \angle (yy_0,x_0y_0) + o(t+\tau )$$ Hence we complete the proof

(2) Now we will apply to our case : If we have $(H,d)$, then define $$(X,|\ |)= \underbrace{(H,d)\times \cdots \times (H,d)}_{n\ times}$$ where $$|(p_i)(p_i')| := \sqrt{ \sum_i d(p_i,p_i')^2 }$$

Then we have a convex set $K:=\Delta H$ And let $f((p_i)):=|(p_i)K|=\inf_{x\in H}\ \sqrt{\sum_i d(p_i,x)^2}$

By (1) since $\pi$ is distance nonexpanding so $$\pi:X\rightarrow K,\ \pi((x_i))=(a_i),\ a_i=x_0,\ \pi((y_i))=(b_i),\ b_i=y_0$$ $$\sqrt{n}d(x_0,y_0) \leq \sqrt{\sum_i d(x_i,y_i)^2}$$

This complete the proof

• What does it have to do with the barycenters? I do not understand and all. – Moishe Kohan Oct 29 '16 at 21:03
• Frankly I did not understand your question Please point more precisely – HK Lee Oct 30 '16 at 10:28
• I think I understand now: The point $x_0$ is the barycenter of $x_1,...,x_n$ and $y_0$ is the barycenter of $y_1,...,y_n$. You compute the barycenter of an $n$-tuple as its projection to the diagonal. Nice argument! – Moishe Kohan Oct 30 '16 at 12:59
• Yes, it is the plan for the proof. And thank you for your comment – HK Lee Oct 30 '16 at 13:03