Abstract
The Schrödinger problem is obtained by replacing the mean square distance with the relative entropy in the Monge–Kantorovich problem. It was first addressed by Schrödinger as the problem of describing the most likely evolution of a large number of Brownian particles conditioned to reach an “unexpected configuration”. Its optimal value, the entropic transportation cost, and its optimal solution, the Schrödinger bridge, stand as the natural probabilistic counterparts to the transportation cost and displacement interpolation. Moreover, they provide a natural way of lifting from the point to the measure setting the concept of Brownian bridge. In this article, we prove that the Schrödinger bridge solves a second order equation in the Riemannian structure of optimal transport. Roughly speaking, the equation says that its acceleration is the gradient of the Fisher information. Using this result, we obtain a fine quantitative description of the dynamics, and a new functional inequality for the entropic transportation cost, that generalize Talagrand’s transportation inequality. Finally, we study the convexity of the Fisher information along Schrödigner bridges, under the hypothesis that the associated reciprocal characteristic is convex. The techniques developed in this article are also well suited to study the Feynman–Kac penalisations of Brownian motion.
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Notes
\(\mathbf {P}\) may not be a probability measure, but an infinite measure. This won’t be a problem as long as \(\mathbf {Q}\) is a probability measure. The note [34] takes care of this issue in detail.
Schrödinger writes in [49] “un écart spontané et considerable par rapport à cette uniformité” .
We recall that \((v_t)\) is the velocity field of \((\mu _t)\), \(\langle \cdot , \cdot \rangle _{\mathbf {T}_{\mu _t}}\) is the inner product in \(L^2_{\mu _t}\) and \(\frac{\mathbf {D}}{dt}\) the covariant derivative. We also denote \(Dv_t\) the Jacobian matrix of \(v_t\). Finally, we abbreviate \(\partial _{x_i}\) with \(\partial _i\), and adopt the same convention for higher-order derivatives.
In the original result of Benamou and Brenier \(v_t\) is not the velocity field of \((\mu _t)\), but just an arbitrary weak solution to the continuity equation. However, it is easy to see that the representation formula for the Wasserstein distance remains true if we restrict the minimization to the couples \((\mu _t,v_t)\) such that \((v_t)\) is the velocity field of \((\mu _t)\).
The constant \(\frac{\lambda }{2}\) instead of \(\lambda \) is because the generator \(\mathscr {L}\) has a \(\frac{1}{2}\Delta \) as second order part instead of \(\Delta \).
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Acknowledgements
The author acknowledges support from CEMPI Lille and the University of Lille 1. He also wishes to thank Christian Léonard for having introduced him to the Schrödinger problem, and for many fruitful discussions.
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Appendix
Appendix
The following Lemma has been used in the proof of Theorem 1.4. Here, we denote \(\dot{f},\ddot{f}\) the first and second derivatives of a function on the real line.
Lemma 4.1
Let \(\phi :[0,1]\rightarrow \mathbb {R}\) be twice differentiable on (0, 1) and continuous on [0, 1] .
-
(i)
If \(\ddot{\phi }_t \ge \lambda \dot{\phi }_t\) for all \(t \in (0,1)\), then
$$\begin{aligned} \forall t \in [0,1], \quad \phi _t \le \phi _1 + (\phi _0 - \phi _1 ) \frac{1-\exp (-\lambda (1-t) )}{1-\exp (-\lambda )}.\end{aligned}$$(70) -
(ii)
If \(\ddot{\phi }_t \ge - \lambda \dot{\phi }_t\) for all \(t \in (0,1)\), then
$$\begin{aligned}\forall t \in [0,1], \quad \phi _t \le \phi _0 + (\phi _1 - \phi _0 ) \frac{1-\exp (-\lambda t )}{1-\exp (-\lambda )}. \end{aligned}$$
Note that the rhs of (70) rewrites nicely as \( \frac{\exp (\lambda ) - \exp (\lambda t)}{\exp (\lambda ) -1} \phi _0 + \frac{\exp (\lambda t) - 1}{\exp (\lambda ) -1} \phi _1\).
Proof
We prove only (i), as (ii) follows from (i) with a simple time-reversal argument. Let g be the unique solution of the differential equation
All we have to show is: \(h:= \phi -g\le 0,\) because a direct calculation shows that the solution of (71) is \( \displaystyle { g_t= \phi _1 + (\phi _0 - \phi _1 ) \frac{1-\exp (-\lambda (1-t) )}{1-\exp (-\lambda )}. }\)
We see that \(\ddot{h}_t\ge \lambda \dot{h}_t, \ 0<t<1\) with \(h_0=h_1=0.\) Considering the function \(u_t:=e ^{ -\lambda t}\dot{h}_t,\)\(0\le t\le 1,\) we have \(\dot{u}_t=e^{ -\lambda t}[\ddot{h}_t-\lambda \dot{h}_t]\ge 0,\) which implies that u is increasing, that is:
Suppose ad absurdum that \(h_{t_o}>0\) for some \(0<t_o<1.\) As \(h_0=0,\) there exists some \(0< t_*\le t_o\) such that \(h_{t_*}>0\) and \(\dot{h}_{t_*}>0.\) In view of (72), this implies that h is increasing on \([t_*,1].\) In particular, \(h_{1}\ge h_{t_*}>0,\) contradicting \(h_1=0.\) Hence \(h\le 0.\)\(\square \)
1.1 Hessian of the entropy and gradient of the Fisher information
1.1.1 Hessian of the entropy
In this paragraph we make some formal computations, whose aim is to give an explanation for Eq. (40). We assume \(U=0\) for simplicity. Let \(\mu \in \mathcal {C}^{b,+}_{\infty }\) and \(\nabla \varphi \in \mathcal {C}_{\infty }^c \) be fixed. We consider the constant speed geodesic \((\mu _t)\) such that \(\mu _0 = \mu \) and \(v_0 = \nabla \varphi \). Then, by definition
Using the identification of the covariant derivative at Lemma 3.3 and (37) we have that
Using the continuity equation
Evaluating at \(t=0\) and using \(v_0=\nabla \varphi \), we can rewrite the latter as
Therefore, observing that \(\overline{\nabla }_{v_t} \nabla \log \mu _t \big |_{t=0} = \mathbf {Hess} \log \mu (\nabla \varphi )\), we arrive at
Hence, using an integration by parts:
At this point one can use the Bochner–Weitzenböck formula
and the hypothesis (13) to obtain the conclusion.
1.1.2 Gradient of the Fisher information
In this section, we shall make some formal computations to justify (5). As we did before, we assume \(U=0\) for simplicity. Differentiating the relation (38) and using the definition of Hessian we get
By the definition of Hessian
where \((\mu _t)\) is any regular enough curve such that \(\mu _0=\mu \), \(v_0=\nabla ^{\mathcal {W}}\mathscr {H}(\mu )\). From Lemma 3.3 such covariant is the projection on the space of gradient vector fields of
Using the continuity equation in the form \(\partial _t \log \mu _t = - \nabla \cdot v_t - v_t \cdot \nabla \log \mu _t\), and recalling that \(\nabla ^{\mathcal {W}}\mathscr {H}(\mu _t) = \nabla \log (\mu _t)\) we arrive at
On the other hand
Therefore
and since the rhs of this vector field is of gradient type,
which is (5).
1.2 Lemmas 4.2 and 4.3
These Lemmas are needed in the proof of Theorem 1.2 and 1.3.
Lemma 4.2
Let Assumption 1.1 (B) hold. Then \( \mathcal {T}^{\sigma }_{U} (\mu ,\nu )<+\infty \) and the dual representation (2) holds. Moreover
-
(i)
f and g are compactly supported and \(f_t,g_t\) globally bounded on \([0,1] \times \mathbb {R}^d\).
-
(ii)
For any \(1 \le l \le 3 \) and \(\varepsilon \in (0,1)\), there exist constants \(A_{l,\varepsilon },B_{l,\varepsilon }\) such that
$$\begin{aligned} \forall x \in M \sup _{\begin{array}{c} \\ t \in [\varepsilon ,1-\varepsilon ] \end{array} } \sup _{ \begin{array}{c} v_1,..,v_l \in \mathbb {R}^d\\ |v_1|,..,|v_l| \le 1 \end{array}} |\partial _{v_l} \ldots \partial _{v_1} \log f_t(x)| \le A_{l,\varepsilon }+B_{l,\varepsilon }|x|^l , \end{aligned}$$and the same conclusion holds replacing \(f_t\) by \(g_t\).
-
(iii)
For any \(\varepsilon \in (0,1)\) there exists a constant \(A_{2,\varepsilon }\) such that
$$\begin{aligned}\sup _{x \in \mathbb {R}^d, t \in [\varepsilon , 1-\varepsilon ]} \sup _{\begin{array}{c} v_2,v_1 \in \mathbb {R}^d\\ |v_2|,|v_1|\le 1 \end{array}} | \partial _{v_2}\partial _{v_1} \log f_t(x) | \le A_{2,\varepsilon }, \end{aligned}$$
and the same conclusion holds replacing \(f_t\) by \(g_t\).
Proof
Since all statements concerning g are proven in the same way as those for f, we limit ourselves to prove the latter ones. In the proof, we assume that \(\sigma =1\), the proof for the general case being almost identical. The fact that \( \mathcal {T}^{\sigma }_{U} (\mu ,\nu )<+\infty \) can be easily settled using point (b) in [32, Prop. 2.5], whereas the dual representation is obtained from [31, Th 2.8]. Let us show that f is compactly supported. Observe
Since \(g_0 \in \mathcal {C}^{+}_{\infty }\), and \(\frac{d\mu }{d\mathbf {m}}\) is compactly supported, f must have the same support as \( \frac{d\mu }{d\mathbf {m}}\). Moreover, since \(g_0\) is bounded from below on the support of f, the fact that \(\frac{d\mu }{d\mathbf {m}}\) is bounded from above, implies that f is bounded from above. It follows from the very definition of \(f_t\) that they must be bounded as well. The proof of (i) is complete. We only do the proof of (ii) and (iii) in the case \(d=1\). This proof can be extended with no difficulty to the general case. We first make some preliminary observations. For \(\alpha \) fixed, the transition density of the Ornstein-Uhlenbeck semigroup is
where
The derivatives of \(\phi \) can be computed using the Hermite polynomials \((H_m)_{m \ge 0}\). We have
Thus, we obtain the following formula for the m-th derivative of the transition density w.r.t. x:
Finally, observe that we can rewrite \(f_t\) equivalently in the form
Let us now prove (ii). Fix \(1 \le l \le 3\). Using (74), we can write \(\partial ^l_x \log f_t(x)\) as a sum of finitely terms of the form
where \(k \le l\) and \(i_1,..,i_k\) are integers summing up to l. Plugging (73) in this expression, the desired conclusion follows using the fact that \(H_m\) is a polynomial of degree m, f is compactly supported, and \(\gamma (\alpha ,t)\) is uniformly bounded from above and below for \(t \in [\varepsilon ,1-\varepsilon ]\). To prove (iii), we compute explicitly \(\partial ^2_x \log f_t(x)\), using (73):
Using the explicit form of the first two Hermite polynomials and some standard calculations, the latter expression is seen to be equal to
The conclusion then follows using the fact that f is compactly supported and that \(\gamma (\alpha ,t)\) is uniformly bounded from above and below for \(t \in [\varepsilon ,1-\varepsilon ]\). \(\square \)
Lemma 4.3
Let Assumption 1.1(A) hold. Then the dual representation 2 holds and
-
(i)
\( \mathcal {T}^{\sigma }_{U} (\mu ,\nu ) < \infty \)
-
(ii)
For any \(\varepsilon \in (0,1)\)\(f_t,g_t,\tilde{f}_t,\tilde{g}_t\) are \(\mathcal {C}^{b,+}_{\infty }\) over \(D_{\varepsilon }\) and \(\log f_t, \log g_t, \log \tilde{f}_t,\log \tilde{g}_t\) are \(\mathcal {C}^{b}_{\infty }\) over \(D_{\varepsilon }\).
Proof
Let \(\varphi _{\mu } = \frac{d \mu }{d \mathbf {m}}\),\(\varphi _{\nu } = \frac{d \nu }{d \mathbf {m}}\) and define \(\pi \in \Pi (\mu ,\nu )\) by \(\pi (x,y) = \varphi _{\mu }(x) \varphi _{\nu }(y) \mathbf {m}\otimes \mathbf {m}(dx dy)\). The theory of Malliavin calculus ensures that \((X_0,X_1)_{\#} \mathbf {P}\) is an absolutely continuous measure on \(M \times M\) with positive smooth density. Since M is compact, then \((X_0,X_1)_{\#} \mathbf {P}\) is equivalent to \( \mathbf {m}\otimes \mathbf {m}\). Therefore, for some constant \(C<+\infty \),
Thus \( \mathcal {T}^{\sigma }_{U} (\mu ,\nu )<+\infty \). It is also a result of Malliavin calculus that \(f_t,g_t\) are of class \(\mathcal {C}^{+}_{\infty }\) on any \(D_{\varepsilon }\). But then, since M is compact, they are also in \(\mathcal {C}^{b,+}_{\infty }\) and uniformly bounded from below, which gives that \(\log f_t,\log g_t \) are in \(\mathcal {C}^{b}_{\infty }\). The statement about \(\tilde{f}_t,\tilde{g}_t\) and their logarithms follows from the one for \(f_t,g_t\) and the compactness of M. \(\square \)
1.3 Proof of Lemma 3.2
Proof
We can rewrite (2) as
Moreover, since \(\mathbf {P}\) is stationary, we have for any t that \({X_t}_{\#} \mathbf {P}= \mathbf {m}\). Therefore
Observing that \(d\mathbf {m}=\exp (-2U) d \mathbf {vol}\), (42) follows from the definition of \(\tilde{f}_t\) and \(\tilde{g}_t\). To prove the second statement, fix \(\varepsilon \in (0,1)\). We observe that, since U is taken to be smooth, the well known results of Malliavin calculus grant that the function \(f_t\) and \(g_t\) are of class \(\mathcal {C}^{+}_{\infty }\), and thus classical solutions on \(D_{\varepsilon }\) of the forward and backward Kolmogorov equations
Using some standard algebraic manipulations and the positivity of \(f_t,g_t\) one finds that \( \tilde{f}_t\) and \( \tilde{g}_t\) are classical solutions on \(D_{\varepsilon }\) of
where \(\mathscr {U}\) was defined at (5). Using this, we prove that \(\frac{1}{2}\nabla (\log g_t-\log f_t) = \frac{1}{2}\nabla (\log \tilde{g}_t-\log \tilde{f}_t)\) is a classical solution to the continuity equation on \(D_{\varepsilon }\). Indeed
Thus, \((t,x)\mapsto \frac{1}{2}\nabla (\log g_t-\log f_t)\) solves the continuity equation, it is of gradient type and, thanks to Lemma 3.1 and (42), \( \sup _{t \in [\varepsilon ,1-\varepsilon ]}\frac{1}{2}| \nabla (\log g_t-\log f_t)|_{\mathbf {T}_{\mu _t}} < + \infty \) also holds. The conclusion then follows. \(\square \)
1.4 Proof of Lemma 3.3
Proof
Fix \(\varepsilon \in (0,1)\). As a preliminary step, we compute \(\partial _t \tau ^{\varepsilon }_t(\xi _t)\). Using the group property we get
where \(o(h)/h \rightarrow 0\) as \(h \rightarrow 0\). Recalling Definition 2.3 and the definition of flow map we get
Therefore, we have shown that, as a pointwise limit
which implies that
Let us now prove the absolute continuity of \((\xi _t)\) along \((\mu _t)\) using what we have just shown. We have
Using (44), the desired absolute continuity follows. Let us now turn to the proof of (45). By definition,
where the limit is in \(L^2_{\mu _t}\). But then, it is also the pointwise limit along a subsequence. Such computation has been done at (77), and yields the desired result. The identity (46) is a direct consequence of (45) and the fact that \(v_t\) is a gradient vector field. \(\square \)
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Conforti, G. A second order equation for Schrödinger bridges with applications to the hot gas experiment and entropic transportation cost. Probab. Theory Relat. Fields 174, 1–47 (2019). https://doi.org/10.1007/s00440-018-0856-7
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DOI: https://doi.org/10.1007/s00440-018-0856-7
Mathematics Subject Classification
- 60J60
- 39B62
- 60F10
- 46N10
- 47D07