In the whole post I will work in the flat torus $\mathbb T^d=\mathbb R^d/\mathbb Z^d$ and $\rho$ will stand for any probability measure $\mathcal P(\mathbb T^d)$. This question is strongly related to two of my previous posts, universal-decay-rate-of-the-fisher-information-along-the-heat-flow and improved-regularization-for-lambda-convex-gradient-flows.

  • Fact 0: the quadratic Wasserstein distance $W_2$ induces a (formal) Riemannian structure on the space of probability measures, which gives a meaning to Wasserstein gradients $\operatorname{grad}_{W_2}F(\rho)$ of a functional $F:\mathcal P(\mathbb T^d)\to\mathbb R$ at a point $\rho$

  • Fact 1: the heat flow $\partial_t\rho_t=\Delta\rho_t$ is the Wasserstein gradient flow $$ \dot\rho_t=-\operatorname{grad}_{W_2}H(\rho_t) $$ of the Boltzmann entropy $$ H(\rho)=\int_{\mathbb T^d}\rho\log\rho $$

  • Fact 2: the Boltzmann entropy is $\lambda$-(displacement) convex for some $\lambda$. Its dissipation functional is the Fisher information, $$ F(\rho):=\|\operatorname{grad}_{W_2} H(\rho)\|^2_{\rho}=\int _{\mathbb T^d}|\nabla\log\rho|^2 \rho $$

  • Fact 3: for abstract metric gradient flows (in the sense of [AGS]) and $\lambda$-convex functionals $\Phi:X\to\mathbb R\cup\{\infty\}$ one expects a smoothing effect for gradient flows $\dot x_t=-\operatorname{grad}\Phi(x_t)$ in the form \begin{equation} |\nabla\Phi(x_t)|^2\leq \frac{C_\lambda}{t} \Big[\Phi(x_0)-\inf_X\Phi\Big] \tag{R} \end{equation} at least for small times, where $C_\lambda$ depends only on $\lambda$ but not on $x_0$ see e.g. [AG, Proposition 3.22 (iii)].

  • Fact 3': with the same notation as in Fact 3, an alternative regularization can be stated as \begin{equation} |\nabla\Phi(x_t)|^2 \leq \frac{1}{2e^{\lambda t}-1}|\nabla\Phi(y)|^2 +\frac{1}{(\int_0^te^{\lambda s}ds)^2} dist^2(x_0,y), \,\, \forall y\in X \tag{R'} \end{equation}

  • Fact 4: in the Torus the Fisher information decays at a universal rate, i-e there is $C=C_d$ depending on the dimension only such that, for all $\rho_0\in \mathcal P(\mathbb T^d)$ and $t>0$, the solution $\rho_t$ of the heat flow emanating from $\rho_0$ satisfies \begin{equation} F(\rho_t)\leq \frac{C}{t} \tag{*} \end{equation} This follows from the Li-Yau inequality [LY], see this post of mine and F. Baudoin's answer.

Question: is there more to ($*$) than just the convexity of the Boltzmann functional? If the driving functional were upper-bounded $\Phi(x_0)\leq C$ (for all $x_0\in X$) in the regularization estimate (R) then we would immediately get the universal decay $|\nabla \Phi(x_t)|^2\leq \frac{C}{t}$. However, in the specific context of Facts 0-2 it is clearly not true that the Boltzmann entropy is upper-bounded. In fact there are many probability measures with infinite entropy, take e.g. any Dirac mass. Since (R) is optimal, I guess that one cannot simply deduce (*) from general $\lambda$-convexity arguments, and there is more than meets the eyes. But is there any connection? Note that both the Li-Yau inequality and the displacement convexity of the Boltzmann entropy strongly rely on the nonnegative Ricci curvature of the underlying torus.

I tried desperately to use any modified regularization estimate (e.g. R' and variants thereof instead of R), but to no avail so far. I am starting to believe that there is no direct implication, and that the work of Li-Yau is really profoundly ad-hoc (don't get me wrong, I just mean that their results cannot be generalized for abstract gradient-flows, and that their result/proof really leverages the specific structure and setting of the heat flow in Riemannian manifolds, not just any gradient flow). I would immensely appreciate any input or insight!

[AG] Ambrosio, L., & Gigli, N. (2013). A user’s guide to optimal transport. In Modelling and optimisation of flows on networks (pp. 1-155). Springer, Berlin, Heidelberg.

[AGS] Ambrosio, L., Gigli, N., & Savaré, G. (2008). Gradient flows: in metric spaces and in the space of probability measures. Springer Science & Business Media.

[LY] Li, P., & Yau, S. T. (1986). On the parabolic kernel of the Schrödinger operator. Acta Mathematica, 156, 153-201.

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    $\begingroup$ There is a generalized notion of $(K,N)$-convexity of the entropy in Wasserstein space, which takes not only the lower Ricci curvature bound into account, but also the (upper bound on the) dimension. See this article by Erbar, Kuwada and Sturm: arxiv.org/abs/1303.4382 The Li-Yau inequality is also valid on RCD($K,N$) spaces, but I never looked carefully into that, so I don't know if a proof using $(K,N)$-convexity of the entropy (and not the Bakry-Émery criterion) is known. $\endgroup$
    – MaoWao
    Commented Jul 11, 2020 at 22:01
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    $\begingroup$ Maybe a small addition to the comment of MaoWao: The mentioned paper of Bochner inequalities in RCK(K,N) spaces lead to many follow-up results, which contain Li-Yau estimates. One, which contains the universal bound (*) as Theorem 1.1 is arxiv.org/abs/1306.0494 by Garofalo-Mondino. So the missing ingredient in your Facts 1-3 is the dimension N and also in the classical result by Li-Yau the constant C depends on the dimension of the space. Hence, a general $\infty$-dimensional theory for gradient flows cannot give you the desired bound. $\endgroup$ Commented Mar 18, 2021 at 8:58
  • $\begingroup$ That's a really good point! Thank you André. $\endgroup$ Commented Mar 18, 2021 at 11:31
  • $\begingroup$ Hi @leo, do you have any updated news on obtaining $(*)$ with the machinery of gradient flows? $\endgroup$
    – Akira
    Commented Nov 20, 2023 at 8:58
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    $\begingroup$ @Akira no, I moved on and completely left this topic aside $\endgroup$ Commented Nov 20, 2023 at 17:56

1 Answer 1


I won't say that it is impossible, but I don't see how to obtain $(\ast)$ using only general theory. There might be a different strategy that works, but I can tell you why I don't think the Li-Yau estimate can be proven using general properties of convexity. In particular, Li-Yau relies on some careful estimates involving the Laplace-Beltrami operator (and some hard analysis), which I don't think general theory can "see".

For a detailed write up of the Li-Yau estimate, I recommend Lectures on Differential Geometry by Schoen and Yau, which was very helpful for me.  From a high level overview, the idea is to let $u$ be a non-negative solution to the heat equation, consider $\log (u + \epsilon)$ and try to bound its derivative. To do this, you consider the point which maximizes $ | \nabla \log (u + \epsilon) |^2$ and use the Bochner formula. Bochner's formula has a correction term due to the curvature, but when the manifold is Ricci positive, this has a favorable sign and we can ignore it (or use something like a barrier function to sharpen the estimate). The key insight is actually a clever use of the Cauchy-Schwarz inequality to eke out a little bit extra from the second derivative terms. It's elementary, but also a stroke of genius, and allows everything else to work.

If you read proofs of Li-Yau, the logarithm tends to appear near the end. However, it was helpful for my intuition to realize that this is not ad hoc; there was always going to be a logarithm because we are using the maximum principle applied to the function $\dfrac{|\nabla u|^2}{(u+\epsilon)^2} = | \nabla \log(u+\epsilon)|^2$.

The fact that $\nabla u$ and $u$ are raised to the same power here is crucial. When the power of $\nabla u$ is less than $u$, integrating out the resulting inequality gives a bounded function (which is significantly less useful). There's this really delicate balancing act in order for everything to work, and logs play an essential role. As a brief aside, I suspect you get different powers of $\nabla u$ and $u$ if you try the Li-Yau strategy with the porous media equation (I'm not entirely sure of this though).

So back to your question about whether this can be done using general properties of gradient flows. It might be a lack of imagination on my part, but it's hard for me to see how this would work. There's several essential steps that rely on hard analysis. For instance, you really need the Cauchy-Schwarz step to work and the resulting function that you get from integrating out should be unbounded. Furthermore, while it's possible to sharpen the estimate, the original version is already fairly sharp, in that there is not a whole lot of wiggle room. As such, while it's possible to adapt the argument to elliptic operators or to include lower order terms, it does seem like there is genuinely more here than the general theory. 

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    $\begingroup$ Thanks @Gabe for your input. I'm fully aware that Li-Yau relies on hard analysis (a clever comparison principle, really). One might argue that the heat flow is not the only flow with this "universal regularization" property (take e.g. any Fokker-Planck). Of course the heat flow is not only a "good" gradient flow but also has super extra-nice properties, which of course everyone will agree upon. So maybe you're right. However I'm still not fully convinced: This whole gradient-flow machinery actually allows to conclude in any sub-levelset of the entropy, so there is something going on here. $\endgroup$ Commented Apr 30, 2020 at 23:09
  • $\begingroup$ That's true. It's definitely possible that there's some deeper fact that allows this to go through. If you can find a way to prove $(\ast )$ without using Li-Yau it would be very neat and likely have some interesting consequences. $\endgroup$
    – Gabe K
    Commented May 1, 2020 at 0:04
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    $\begingroup$ Well, truth be told I'm pretty sure I can't do that. What I'd like to understand is: Which $\epsilon$-improvement is still missing to conclude? OK, maybe I'm still missing $10\epsilon$ or $\sqrt\epsilon$, but I'm pretty sure that any result in this direction (thus using mainly quantitative convexity arguments) would give a whole new perspective on the problem! Also, understanding what is missing would help identifying some hidden structural properties, I feel. In a nutshell: How much better is the heat flow than just any $\lambda$-convex gradient flow? It seems to me that not too much... $\endgroup$ Commented May 1, 2020 at 0:11

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