# Convergence in Distribution of Some Self-Interacting Diffusions

Authors: Aline Kurtzmann
Journal: Journal of Probability and Statistics
 (2014)

---

## Abstract

The present paper is concerned with some self-interacting diffusions(
X
t
,
t
≥
0
) living on ℝ
d. These diffusions are solutions to stochastic differential equations: d
X
t
=
d
B
t
-
g
(
t
)
∇
V
(
X
t
-
μ
¯
t
)
d
t, where μ
¯
t is the empirical mean of the process X, V is an asymptotically strictly convex potential, and g is a given positive function. We study the asymptotic behaviour of X for three different families of functions g. If g
t
=
k
log
⁡
t with k small enough, then the process X converges in distribution towards the global minima of V, whereas if t
g
(
t
)
→
c
∈
]
0
,
+
∞
] or if g
(
t
)
→
g
(
∞
)
∈
[
0
,
+
∞
[, then X converges in distribution if and only if∫
x
e
-
2
V
(
x
)
d
x
=
0.


---

## Body

## 1. Introduction

The aim of this paper is to obtain necessary and sufficient conditions for the convergence in distribution of a self-interacting diffusion living onℝ
d. Consider a smooth potential V
:
ℝ
d
→
ℝ
+ and a map g
:
ℝ
+
→
ℝ
+. We study the asymptotic behaviour of the self-interacting diffusion X given by
(1)
d
X
t
=
d
B
t
-
g
(
t
)
∇
V
(
X
t
-
μ
¯
t
)
d
t
,
X
0
=
x
=
μ
¯
0
,
where B is a standard Brownian motion and μ
¯
t denotes the empirical mean of the process X:
(2)
μ
¯
t
=
1
t
∫
0
t
‍
X
s
d
s
.
This is a model of reinforcement that could be used to represent the (simplified) behaviour of some social insects. Some insects, as ants, mark their paths with pheromones. This serves as a guide for other ants to return to the nest. The trail of pheromones is denoted by X and its evaporation by g. Despite this evaporation, the path is reinforced and the insects gradually manage to find the best route.The same model has been already studied by Chambeu and Kurtzmann [1], in case of an unbounded increasing function g. The authors have proven that, under certain conditions, the process satisfies a kind of pointwise ergodic theorem and that if V admits a unique minimum at 0, then X
t converges almost surely. In this paper, we do not suppose that either g increases to the infinity nor that V admits a unique minimum at 0. This will obviously change the asymptotic behaviour of X, even if X will converge in distribution in most of the cases. We will essentially use two different techniques here. The first one is the well-known theory of simulated annealing, which has been developed a lot since the 80s with a huge literature, whereas the second one is simply a change of scale added to a change of “speed measure.”Let us explain briefly the simulated annealing method. An important question for physical systems is to find the globally minimum energy states of the system. Experimentally, the ground states are reached by chemical annealing. One first melts a substance and then cools it slowly, being careful to pass slowly through the freezing temperature. If the temperature decreases too rapidly, then the system does not end up in a ground state, but in a local nonglobal minimum. On the other hand, if the temperature decreases too slowly, then the system approaches the ground states very slowly. The competition between these two effects determines the optimal speed of cooling, that is, the annealing schedule. The study of simulated annealing has involved the theory of nonhomogeneous Markov chains and diffusion processes, large deviation theory, spectral analysis of operators, and singular perturbation theory. Pioneering work was done by Freidlin and Wentzell [2]. The initial problem consists in finding the global minima of a given function U. Actually, one has to study the diffusion Markov process X
ε in ℝ
d given by the Langevin-type Markov diffusion d
X
t
ε
=
d
B
ε
t
-
∇
U
(
X
ε
)
d
t. If the temperature ε is constant for a sufficiently large amount of time, then the process X
ε and the fixed temperature process behave approximatively the same at the end of that time interval. The optimal annealing schedule, that is, ε for the convergence criterion ℙ
(
X
t
ε
∈
Min
⁡
)
→
t
→
∞
1, where Min
⁡ denotes the set of all the global minima of U, was first determined by Hajek [3] for a finite state space. Chiang et al. [4] studied the convergence rate of ℙ
x
(
X
t
ε
∈
·
) via the large deviations of the transition density of X
ε. They were one of the first to show the convergence of the algorithm of the simulated annealing for ε
t
2
=
k
/
log
⁡
⁡
t, for k large enough, related to the second eigenvalue of the corresponding (to X
ε) infinitesimal generator. Finally, Holley and Stroock [5] initiated a new method and proved, in the discrete case, the convergence of the simulated annealing algorithm via the Sobolev inequality. They went further in their study with Holley et al. [6]. Later, Miclo [7] proved, through some functional inequalities, that the free energy (i.e., the relative entropy of the distribution of the process at time t with respect to the invariant probability measure for the elliptic operator considered as a time-homogeneous operator by fixing t) satisfies a differential inequality, which implies (under some decreasing evolution of the temperature to zero) the convergence of the process to the global minima of the potential. And if the temperature ε decreases too fast to zero, then the potential can freeze in a local minimum (depending on the initial condition) and so the process converges to this local minimum.We begin to study theℝ
d-valued Markov process Y
t
:
=
X
t
-
μ
¯
t, which satisfies the following SDE:
(3)
d
Y
t
=
d
B
t
-
g
(
t
)
∇
V
(
Y
t
)
d
t
-
Y
t
d
t
t
,
Y
0
=
0
;
d
μ
¯
t
=
Y
t
d
t
t
,
μ
¯
0
=
x
.
We will adapt the simulated annealing method to Y for functions g large enough (i.e., g does not go to zero) to prove the convergence in distribution of Y.We wish to point out that a one-dimensional Brownian motion in a time-dependent potential has been recently studied by Gradinaru and Offret [8]:
(4)
d
Z
t
=
d
B
t
-
∂
x
V
ρ
,
α
,
β
(
t
,
Z
t
)
d
t
,
Z
t
0
=
z
0
with V
ρ
,
α
,
β
(
t
,
x
)
=
(
ρ
/
(
α
+
1
)
)
(
|
x
|
α
+
1
/
t
β
)
1
α
≠
-
1
+
ρ
(
log
⁡
⁡
|
x
|
/
t
β
)
1
α
=
-
1 and z
0
,
ρ
,
α
,
β
∈
ℝ. This is quite close in spirit to the study of our process Y, even if the authors suppose in [8] that both V and 1
/
g are polynomial. They obtain conditions for the recurrence, transience, and convergence of the studied process Z. We refer to the survey of Ivanov et al. [9] for the existence and uniqueness of solutions to such equations. In the present paper, we do not suppose that g is polynomial and the dimension is d
≥
1 and thus we obtain less precise results.The remainder of the paper is organized as follows. First, in Section2, we introduce some useful tools, such as the logarithmic Sobolev inequality and the Kullback information. They both will be needed for the simulated annealing study. Section 3 is devoted to the simulated annealing method in the case when g behaves asymptotically as k
log
⁡
t. In this part, we will prove the (pointwise) ergodicity of the process Y and the convergence in distribution of X, depending on the potential V. Finally, Section 4 deals with the convergence in distribution of X when t
g
(
t
)
→
+
∞ and g
(
t
)
→
c
≥
0, depending on the asymptotics of V.


## 2. Some Useful Tools

### 2.1. Assumptions and Existence

In the following,(
·
,
·
) denotes the Euclidean scalar product. We denote by 𝒫
(
ℝ
d
) the set of probability measures on ℝ
d. We denote by G the function G
(
t
)
=
∫
0
t
g
(
s
)
d
s. We assume that the mapping g
:
ℝ
+
→
ℝ
+ is 𝒞
1
(
ℝ
+
). The precise hypothesis on g will be given at the beginning of each section.In the sequel, the technical assumptions on the potentialV
:
ℝ
d
→
ℝ
+ are the following:(1)
(regularity and positivity)
V
∈
𝒞
2
(
ℝ
d
) and V
≥
0;
(2)
(convexity)
V
=
W
+
χ, where W is C
W(>0)-strictly uniformly convex and χ is a compactly supported function and there exists C
χ
>
0 such that ∇
χ is C
χ-Lipschitz;
(3)
(growth) there exists a
>
0 such that for all x
∈
ℝ
d, we have
(5)
Δ
V
(
x
)
≤
a
V
(
x
)
,
lim
⁡
|
x
|
→
+
∞
|
∇
V
(
x
)
|
2
V
(
x
)
=
+
∞
.We also assume that V has a finite number of critical points. Let Max
⁡
=
{
M
1
,
M
2
,
…
,
M
p
} be the set of the saddle points and local maxima of V and let Min
⁡
=
{
m
1
,
m
2
,
…
,
m
n
} be the set of the local minima of V, such that the Hessian matrix is nondegenerate for all local minimum. Without any loss of generality, we suppose that min
⁡
⁡
V
=
0.Remark 1.
The caseV of quadratic growth is excluded here, as it has been fully studied in [1].Let us first prove the global strong existence and uniqueness of the processX.Proposition 2.
For anyx
∈
ℝ
d
,
μ
∈
𝒫
(
ℝ
d
), there exists a unique global strong solution (
X
t
,
t
≥
0
) of (1).Proof.
The local existence and uniqueness of such a process is standard. We only need to prove here thatY, hence, X (because X
t
:
=
x
+
Y
t
+
∫
0
t
Y
s
(
d
s
/
s
)), does not explode in a finite time. To this aim, we apply Itô's formula to the function x
↦
V
(
x
):
(6)
d
V
(
Y
t
)
=
(
∇
V
(
Y
t
)
,
d
B
t
)
+
(
1
2
Δ
V
(
Y
t
)
-
g
(
t
)
|
∇
V
(
Y
t
)
|
2
-
1
t
(
∇
V
(
Y
t
)
,
Y
t
)
)
d
t
,
and introduce the sequence of stopping times τ
0
=
0 and
(7)
τ
n
=
inf
⁡
⁡
{
t
≥
0
;
V
(
Y
t
)
>
n
}
.
By the convexity condition, we have (
∇
V
(
y
)
,
y
)
→
|
y
|
→
+
∞
+
∞, and by the condition (5), there exists C
>
0 such that 𝔼
V
(
Y
t
∧
τ
n
)
≤
𝔼
V
(
Y
0
)
+
C
t.


### 2.2. Preliminaries

#### 2.2.1. Logarithmic Sobolev Inequality

Definition 3.
The probability measureμ satisfies the logarithmic Sobolev inequality, with the constant C
LS denoted by LSI
(
C
LS
), if for all function h
∈
L
2
(
μ
), we have
(8)
∫
h
2
log
⁡
⁡
h
2
d
μ
-
(
∫
h
2
d
μ
)
log
⁡
⁡
(
∫
h
2
d
μ
)
≤
C
LS
∫
|
∇
h
|
2
d
μ
.
Letp
(
s
,
x
,
t
,
y
) denote the density of the semigroup corresponding to the nonhomogeneous Markov process Z defined by
(9)
d
Z
t
=
ε
t
d
B
t
-
(
∇
V
(
Z
t
)
+
Z
t
a
t
)
d
t
.
We will specify later the precise form of ε
t and a
t. We associate to this process the probability measure Π
t
,
ε
t
(
d
x
)
=
(
1
/
π
t
)
exp
⁡
⁡
{
-
2
ε
t
-
2
(
V
(
x
)
+
|
x
|
2
/
2
a
t
)
}
d
x, where π
t is the normalization constant of Π
t
,
ε
t.Lemma 4.
The family of probability measures(
Π
t
,
ε
t
,
t
≥
0
) satisfies a logarithmic Sobolev inequality L
S
I
(
C
(
t
)
).Proof.
We use the celebrated Bakry-EmeryΓ
2-criterion, see [10]. We recall that, to the operator L
t
,
ε
t, we associate the operator “carré du champ”; that is, (for all function f
,
g
∈
𝒞
∞)
(10)
Γ
t
V
(
f
,
g
)
:
=
1
2
(
L
t
,
ε
t
(
f
g
)
-
f
L
t
,
ε
t
g
-
g
L
t
,
ε
t
f
)
.
Then, we define the operator Γ
2
V as
(11)
Γ
2
V
(
t
)
(
f
)
:
=
1
2
(
L
t
,
ε
t
Γ
t
V
(
f
,
f
)
-
2
Γ
t
V
(
f
,
L
t
,
ε
t
f
)
)
.
The Γ
2-criterion asserts that if there exists a positive constant C such that Γ
2
V
t
≥
C
Γ
t
V
t, then Π
t
,
ε
t satisfies a logarithmic Sobolev inequality, with the constant 2
/
C.
An easy calculation, for any functionf of class 𝒞
∞, leads to
(12)
Γ
t
V
(
f
,
f
)
=
ε
t
2
|
∇
f
|
2
,
Γ
2
V
(
t
)
(
f
)
=
ε
t
2
2
(
∇
f
,
∇
2
V
∇
f
)
+
ε
t
4
4
∥
∇
2
f
∥
2
+
ε
t
2
2
a
(
t
)
|
∇
f
|
2
.
AsV (and also V
t) is strictly convex off a compact set, we have the decomposition V
=
W
+
χ as in the convexity hypothesis. We apply the Γ
2-criterion of Bakry-Emery to the function W and we get that Γ
2
W
(
t
)
(
f
)
≥
C
W
Γ
t
W
(
f
). Thus, the probability measure e
-
2
ε
t
-
2
(
W
(
x
)
+
|
x
|
2
/
a
(
t
)
)
/
π
t satisfies the inequality LSI
(
2
/
C
W
). We conclude, by the perturbation lemma due to Holley and Stroock [11], that the measure Π
t
,
ε
t satisfies a Sobolev logarithmic inequality with a constant less than or equal to 2
e
(
2
/
ε
t
2
)
osc
χ
/
C
W, where osc
(
χ
)
=
sup
⁡
χ
-
inf
⁡
χ.


#### 2.2.2. Kullback Information

Definition 5.
We define the free energy (up to an additive constant), known as the relative Kullback information, of a probability measureν with respect to a probability measure Π by
(13)
H
(
ν
∣
Π
)
:
=
∫
d
ν
log
⁡
⁡
d
ν
d
Π
.
If we suppose that ν (resp., Π) has the density ν (resp., π) with respect to the Lebesgue measure λ, then one has
(14)
H
(
ν
∣
Π
)
:
=
∫
ν
log
⁡
⁡
ν
π
d
λ
.In this paper, we will first prove the decrease to zero of the relative free energy of the law ofZ
t with respect to Π
t
,
ε
t. The classical Csiszár-Kullback-Pinsker inequality relates the total variation norm to the free energy in the following way (see for instance [5]):
(15)
∥
μ
-
ν
∥
TV
≤
2
H
(
μ
∣
ν
)
.
So as the total variation norm metrizes the convergence in distribution, once we have proven that the measure Π
t
,
ε
t converges weakly to a measure Π and H
(
p
t
∣
Π
t
,
ε
t
) goes to zero, then the distribution of Z
t converges to Π. As Z
t is the time-shifted process Y
t, we obtain this way that Y converges in distribution to Π.Our strategy to show thatH
(
p
t
∣
H
t
,
ε
t
) goes to zero is the following. To shorten notation, let p
t
:
=
p
(
t
0
,
x
0
,
t
,
·
) be the distribution law of the process Z
t conditioned on Z
t
0
=
x
0. We recall that the family of probability measures (
Π
t
,
ε
t
,
t
≥
0
) satisfies a Sobolev logarithmic inequality LSI
(
C
(
t
)
). We have also Π
t
,
ε
t
(
d
x
)
=
π
t
,
ε
t
(
x
)
λ
(
d
x
). So, we choose h
t
=
p
t
/
π
t
,
ε
t satisfying ∫
‍
h
t
2
d
Π
t
,
ε
t
=
1 and we will show in Corollary 13 the existence of C
(
t
)
>
0 such that
(16)
H
(
p
t
∣
Π
t
,
ε
t
)
=
∫
p
t
log
⁡
⁡
p
t
π
t
,
ε
t
d
λ
≤
C
(
t
)
∫
|
∇
h
t
|
2
d
Π
t
,
ε
t
.


#### 2.2.3. Asymptotic Pseudotrajectories

In Section4, we will use the notion of asymptotic pseudotrajectory, introduced by Benaïm and Hirsch [12]. It is particularly useful to analyze the long-term behaviour of stochastic processes, considered as approximations of solutions of ordinary differential equation (the ODE method).Definition 6.
The processY is an asymptotic pseudotrajectory for the flow ϕ if for all T
>
0,
(17)
lim
⁡
t
→
+
∞
sup
⁡
0
≤
s
≤
T
|
Y
t
+
s
-
ϕ
s
(
Y
t
)
|
=
0
a
.
s
.It is shown in [12] that if Y is an asymptotic pseudotrajectory for ϕ, then the ω-limit set of the flow generated by ϕ is the same as the ω-limit set of the process Y.


## 2.1. Assumptions and Existence

In the following,(
·
,
·
) denotes the Euclidean scalar product. We denote by 𝒫
(
ℝ
d
) the set of probability measures on ℝ
d. We denote by G the function G
(
t
)
=
∫
0
t
g
(
s
)
d
s. We assume that the mapping g
:
ℝ
+
→
ℝ
+ is 𝒞
1
(
ℝ
+
). The precise hypothesis on g will be given at the beginning of each section.In the sequel, the technical assumptions on the potentialV
:
ℝ
d
→
ℝ
+ are the following:(1)
(regularity and positivity)
V
∈
𝒞
2
(
ℝ
d
) and V
≥
0;
(2)
(convexity)
V
=
W
+
χ, where W is C
W(>0)-strictly uniformly convex and χ is a compactly supported function and there exists C
χ
>
0 such that ∇
χ is C
χ-Lipschitz;
(3)
(growth) there exists a
>
0 such that for all x
∈
ℝ
d, we have
(5)
Δ
V
(
x
)
≤
a
V
(
x
)
,
lim
⁡
|
x
|
→
+
∞
|
∇
V
(
x
)
|
2
V
(
x
)
=
+
∞
.We also assume that V has a finite number of critical points. Let Max
⁡
=
{
M
1
,
M
2
,
…
,
M
p
} be the set of the saddle points and local maxima of V and let Min
⁡
=
{
m
1
,
m
2
,
…
,
m
n
} be the set of the local minima of V, such that the Hessian matrix is nondegenerate for all local minimum. Without any loss of generality, we suppose that min
⁡
⁡
V
=
0.Remark 1.
The caseV of quadratic growth is excluded here, as it has been fully studied in [1].Let us first prove the global strong existence and uniqueness of the processX.Proposition 2.
For anyx
∈
ℝ
d
,
μ
∈
𝒫
(
ℝ
d
), there exists a unique global strong solution (
X
t
,
t
≥
0
) of (1).Proof.
The local existence and uniqueness of such a process is standard. We only need to prove here thatY, hence, X (because X
t
:
=
x
+
Y
t
+
∫
0
t
Y
s
(
d
s
/
s
)), does not explode in a finite time. To this aim, we apply Itô's formula to the function x
↦
V
(
x
):
(6)
d
V
(
Y
t
)
=
(
∇
V
(
Y
t
)
,
d
B
t
)
+
(
1
2
Δ
V
(
Y
t
)
-
g
(
t
)
|
∇
V
(
Y
t
)
|
2
-
1
t
(
∇
V
(
Y
t
)
,
Y
t
)
)
d
t
,
and introduce the sequence of stopping times τ
0
=
0 and
(7)
τ
n
=
inf
⁡
⁡
{
t
≥
0
;
V
(
Y
t
)
>
n
}
.
By the convexity condition, we have (
∇
V
(
y
)
,
y
)
→
|
y
|
→
+
∞
+
∞, and by the condition (5), there exists C
>
0 such that 𝔼
V
(
Y
t
∧
τ
n
)
≤
𝔼
V
(
Y
0
)
+
C
t.


## 2.2. Preliminaries

### 2.2.1. Logarithmic Sobolev Inequality

Definition 3.
The probability measureμ satisfies the logarithmic Sobolev inequality, with the constant C
LS denoted by LSI
(
C
LS
), if for all function h
∈
L
2
(
μ
), we have
(8)
∫
h
2
log
⁡
⁡
h
2
d
μ
-
(
∫
h
2
d
μ
)
log
⁡
⁡
(
∫
h
2
d
μ
)
≤
C
LS
∫
|
∇
h
|
2
d
μ
.
Letp
(
s
,
x
,
t
,
y
) denote the density of the semigroup corresponding to the nonhomogeneous Markov process Z defined by
(9)
d
Z
t
=
ε
t
d
B
t
-
(
∇
V
(
Z
t
)
+
Z
t
a
t
)
d
t
.
We will specify later the precise form of ε
t and a
t. We associate to this process the probability measure Π
t
,
ε
t
(
d
x
)
=
(
1
/
π
t
)
exp
⁡
⁡
{
-
2
ε
t
-
2
(
V
(
x
)
+
|
x
|
2
/
2
a
t
)
}
d
x, where π
t is the normalization constant of Π
t
,
ε
t.Lemma 4.
The family of probability measures(
Π
t
,
ε
t
,
t
≥
0
) satisfies a logarithmic Sobolev inequality L
S
I
(
C
(
t
)
).Proof.
We use the celebrated Bakry-EmeryΓ
2-criterion, see [10]. We recall that, to the operator L
t
,
ε
t, we associate the operator “carré du champ”; that is, (for all function f
,
g
∈
𝒞
∞)
(10)
Γ
t
V
(
f
,
g
)
:
=
1
2
(
L
t
,
ε
t
(
f
g
)
-
f
L
t
,
ε
t
g
-
g
L
t
,
ε
t
f
)
.
Then, we define the operator Γ
2
V as
(11)
Γ
2
V
(
t
)
(
f
)
:
=
1
2
(
L
t
,
ε
t
Γ
t
V
(
f
,
f
)
-
2
Γ
t
V
(
f
,
L
t
,
ε
t
f
)
)
.
The Γ
2-criterion asserts that if there exists a positive constant C such that Γ
2
V
t
≥
C
Γ
t
V
t, then Π
t
,
ε
t satisfies a logarithmic Sobolev inequality, with the constant 2
/
C.
An easy calculation, for any functionf of class 𝒞
∞, leads to
(12)
Γ
t
V
(
f
,
f
)
=
ε
t
2
|
∇
f
|
2
,
Γ
2
V
(
t
)
(
f
)
=
ε
t
2
2
(
∇
f
,
∇
2
V
∇
f
)
+
ε
t
4
4
∥
∇
2
f
∥
2
+
ε
t
2
2
a
(
t
)
|
∇
f
|
2
.
AsV (and also V
t) is strictly convex off a compact set, we have the decomposition V
=
W
+
χ as in the convexity hypothesis. We apply the Γ
2-criterion of Bakry-Emery to the function W and we get that Γ
2
W
(
t
)
(
f
)
≥
C
W
Γ
t
W
(
f
). Thus, the probability measure e
-
2
ε
t
-
2
(
W
(
x
)
+
|
x
|
2
/
a
(
t
)
)
/
π
t satisfies the inequality LSI
(
2
/
C
W
). We conclude, by the perturbation lemma due to Holley and Stroock [11], that the measure Π
t
,
ε
t satisfies a Sobolev logarithmic inequality with a constant less than or equal to 2
e
(
2
/
ε
t
2
)
osc
χ
/
C
W, where osc
(
χ
)
=
sup
⁡
χ
-
inf
⁡
χ.


### 2.2.2. Kullback Information

Definition 5.
We define the free energy (up to an additive constant), known as the relative Kullback information, of a probability measureν with respect to a probability measure Π by
(13)
H
(
ν
∣
Π
)
:
=
∫
d
ν
log
⁡
⁡
d
ν
d
Π
.
If we suppose that ν (resp., Π) has the density ν (resp., π) with respect to the Lebesgue measure λ, then one has
(14)
H
(
ν
∣
Π
)
:
=
∫
ν
log
⁡
⁡
ν
π
d
λ
.In this paper, we will first prove the decrease to zero of the relative free energy of the law ofZ
t with respect to Π
t
,
ε
t. The classical Csiszár-Kullback-Pinsker inequality relates the total variation norm to the free energy in the following way (see for instance [5]):
(15)
∥
μ
-
ν
∥
TV
≤
2
H
(
μ
∣
ν
)
.
So as the total variation norm metrizes the convergence in distribution, once we have proven that the measure Π
t
,
ε
t converges weakly to a measure Π and H
(
p
t
∣
Π
t
,
ε
t
) goes to zero, then the distribution of Z
t converges to Π. As Z
t is the time-shifted process Y
t, we obtain this way that Y converges in distribution to Π.Our strategy to show thatH
(
p
t
∣
H
t
,
ε
t
) goes to zero is the following. To shorten notation, let p
t
:
=
p
(
t
0
,
x
0
,
t
,
·
) be the distribution law of the process Z
t conditioned on Z
t
0
=
x
0. We recall that the family of probability measures (
Π
t
,
ε
t
,
t
≥
0
) satisfies a Sobolev logarithmic inequality LSI
(
C
(
t
)
). We have also Π
t
,
ε
t
(
d
x
)
=
π
t
,
ε
t
(
x
)
λ
(
d
x
). So, we choose h
t
=
p
t
/
π
t
,
ε
t satisfying ∫
‍
h
t
2
d
Π
t
,
ε
t
=
1 and we will show in Corollary 13 the existence of C
(
t
)
>
0 such that
(16)
H
(
p
t
∣
Π
t
,
ε
t
)
=
∫
p
t
log
⁡
⁡
p
t
π
t
,
ε
t
d
λ
≤
C
(
t
)
∫
|
∇
h
t
|
2
d
Π
t
,
ε
t
.


### 2.2.3. Asymptotic Pseudotrajectories

In Section4, we will use the notion of asymptotic pseudotrajectory, introduced by Benaïm and Hirsch [12]. It is particularly useful to analyze the long-term behaviour of stochastic processes, considered as approximations of solutions of ordinary differential equation (the ODE method).Definition 6.
The processY is an asymptotic pseudotrajectory for the flow ϕ if for all T
>
0,
(17)
lim
⁡
t
→
+
∞
sup
⁡
0
≤
s
≤
T
|
Y
t
+
s
-
ϕ
s
(
Y
t
)
|
=
0
a
.
s
.It is shown in [12] that if Y is an asymptotic pseudotrajectory for ϕ, then the ω-limit set of the flow generated by ϕ is the same as the ω-limit set of the process Y.


## 2.2.1. Logarithmic Sobolev Inequality

Definition 3.
The probability measureμ satisfies the logarithmic Sobolev inequality, with the constant C
LS denoted by LSI
(
C
LS
), if for all function h
∈
L
2
(
μ
), we have
(8)
∫
h
2
log
⁡
⁡
h
2
d
μ
-
(
∫
h
2
d
μ
)
log
⁡
⁡
(
∫
h
2
d
μ
)
≤
C
LS
∫
|
∇
h
|
2
d
μ
.
Letp
(
s
,
x
,
t
,
y
) denote the density of the semigroup corresponding to the nonhomogeneous Markov process Z defined by
(9)
d
Z
t
=
ε
t
d
B
t
-
(
∇
V
(
Z
t
)
+
Z
t
a
t
)
d
t
.
We will specify later the precise form of ε
t and a
t. We associate to this process the probability measure Π
t
,
ε
t
(
d
x
)
=
(
1
/
π
t
)
exp
⁡
⁡
{
-
2
ε
t
-
2
(
V
(
x
)
+
|
x
|
2
/
2
a
t
)
}
d
x, where π
t is the normalization constant of Π
t
,
ε
t.Lemma 4.
The family of probability measures(
Π
t
,
ε
t
,
t
≥
0
) satisfies a logarithmic Sobolev inequality L
S
I
(
C
(
t
)
).Proof.
We use the celebrated Bakry-EmeryΓ
2-criterion, see [10]. We recall that, to the operator L
t
,
ε
t, we associate the operator “carré du champ”; that is, (for all function f
,
g
∈
𝒞
∞)
(10)
Γ
t
V
(
f
,
g
)
:
=
1
2
(
L
t
,
ε
t
(
f
g
)
-
f
L
t
,
ε
t
g
-
g
L
t
,
ε
t
f
)
.
Then, we define the operator Γ
2
V as
(11)
Γ
2
V
(
t
)
(
f
)
:
=
1
2
(
L
t
,
ε
t
Γ
t
V
(
f
,
f
)
-
2
Γ
t
V
(
f
,
L
t
,
ε
t
f
)
)
.
The Γ
2-criterion asserts that if there exists a positive constant C such that Γ
2
V
t
≥
C
Γ
t
V
t, then Π
t
,
ε
t satisfies a logarithmic Sobolev inequality, with the constant 2
/
C.
An easy calculation, for any functionf of class 𝒞
∞, leads to
(12)
Γ
t
V
(
f
,
f
)
=
ε
t
2
|
∇
f
|
2
,
Γ
2
V
(
t
)
(
f
)
=
ε
t
2
2
(
∇
f
,
∇
2
V
∇
f
)
+
ε
t
4
4
∥
∇
2
f
∥
2
+
ε
t
2
2
a
(
t
)
|
∇
f
|
2
.
AsV (and also V
t) is strictly convex off a compact set, we have the decomposition V
=
W
+
χ as in the convexity hypothesis. We apply the Γ
2-criterion of Bakry-Emery to the function W and we get that Γ
2
W
(
t
)
(
f
)
≥
C
W
Γ
t
W
(
f
). Thus, the probability measure e
-
2
ε
t
-
2
(
W
(
x
)
+
|
x
|
2
/
a
(
t
)
)
/
π
t satisfies the inequality LSI
(
2
/
C
W
). We conclude, by the perturbation lemma due to Holley and Stroock [11], that the measure Π
t
,
ε
t satisfies a Sobolev logarithmic inequality with a constant less than or equal to 2
e
(
2
/
ε
t
2
)
osc
χ
/
C
W, where osc
(
χ
)
=
sup
⁡
χ
-
inf
⁡
χ.


## 2.2.2. Kullback Information

Definition 5.
We define the free energy (up to an additive constant), known as the relative Kullback information, of a probability measureν with respect to a probability measure Π by
(13)
H
(
ν
∣
Π
)
:
=
∫
d
ν
log
⁡
⁡
d
ν
d
Π
.
If we suppose that ν (resp., Π) has the density ν (resp., π) with respect to the Lebesgue measure λ, then one has
(14)
H
(
ν
∣
Π
)
:
=
∫
ν
log
⁡
⁡
ν
π
d
λ
.In this paper, we will first prove the decrease to zero of the relative free energy of the law ofZ
t with respect to Π
t
,
ε
t. The classical Csiszár-Kullback-Pinsker inequality relates the total variation norm to the free energy in the following way (see for instance [5]):
(15)
∥
μ
-
ν
∥
TV
≤
2
H
(
μ
∣
ν
)
.
So as the total variation norm metrizes the convergence in distribution, once we have proven that the measure Π
t
,
ε
t converges weakly to a measure Π and H
(
p
t
∣
Π
t
,
ε
t
) goes to zero, then the distribution of Z
t converges to Π. As Z
t is the time-shifted process Y
t, we obtain this way that Y converges in distribution to Π.Our strategy to show thatH
(
p
t
∣
H
t
,
ε
t
) goes to zero is the following. To shorten notation, let p
t
:
=
p
(
t
0
,
x
0
,
t
,
·
) be the distribution law of the process Z
t conditioned on Z
t
0
=
x
0. We recall that the family of probability measures (
Π
t
,
ε
t
,
t
≥
0
) satisfies a Sobolev logarithmic inequality LSI
(
C
(
t
)
). We have also Π
t
,
ε
t
(
d
x
)
=
π
t
,
ε
t
(
x
)
λ
(
d
x
). So, we choose h
t
=
p
t
/
π
t
,
ε
t satisfying ∫
‍
h
t
2
d
Π
t
,
ε
t
=
1 and we will show in Corollary 13 the existence of C
(
t
)
>
0 such that
(16)
H
(
p
t
∣
Π
t
,
ε
t
)
=
∫
p
t
log
⁡
⁡
p
t
π
t
,
ε
t
d
λ
≤
C
(
t
)
∫
|
∇
h
t
|
2
d
Π
t
,
ε
t
.


## 2.2.3. Asymptotic Pseudotrajectories

In Section4, we will use the notion of asymptotic pseudotrajectory, introduced by Benaïm and Hirsch [12]. It is particularly useful to analyze the long-term behaviour of stochastic processes, considered as approximations of solutions of ordinary differential equation (the ODE method).Definition 6.
The processY is an asymptotic pseudotrajectory for the flow ϕ if for all T
>
0,
(17)
lim
⁡
t
→
+
∞
sup
⁡
0
≤
s
≤
T
|
Y
t
+
s
-
ϕ
s
(
Y
t
)
|
=
0
a
.
s
.It is shown in [12] that if Y is an asymptotic pseudotrajectory for ϕ, then the ω-limit set of the flow generated by ϕ is the same as the ω-limit set of the process Y.


## 3. The Simulated Annealing Method

Assume that the mappingg
:
ℝ
+
→
ℝ
+ is asymptotically equivalent (up to a multiplicative positive constant) to log
⁡
⁡
t and satisfies g
∈
𝒞
1
(
ℝ
+
) such that g
(
0
)
>
0 and for all T
>
0, G
-
1
(
t
+
T
)
-
G
-
1
(
t
)
→
t
→
∞
0, where G
-
1 is the generalized inverse of G.Instead of consideringY, we consider the time-changed process Z
t
:
=
Y
G
-
1
(
t
). This last process satisfies the following SDE:
(18)
d
Z
t
=
1
g
∘
G
-
1
(
t
)
d
B
t
-
(
∇
V
(
Z
t
)
+
Z
t
G
-
1
(
t
)
g
∘
G
-
1
(
t
)
)
d
t
,
Z
0
=
Y
G
-
1
(
0
)
=
0
,
where B is a Brownian motion such that ∫
0
t
(
1
/
g
∘
G
-
1
(
s
)
)
d
B
s has the same law as B
G
-
1
(
t
).


### 3.1. Convergence in Distribution towards the Global Minima ofV

We defineε
t
2
=
1
/
(
g
∘
G
-
1
(
t
)
) and a
(
t
)
=
G
-
1
(
t
)
g
∘
G
-
1
(
t
). The process Z satisfies
(19)
d
Z
t
=
ε
t
d
B
t
-
∇
V
t
(
Z
t
)
d
t
,
where we have defined V
t
(
x
)
:
=
V
(
x
)
+
|
x
|
2
/
2
a
(
t
). Actually, we will prove that this nonhomogeneous Markov process converges in distribution to a measure that could correspond to its “invariant” probability measure. Of course, if we suppose that a
(
t
)
≡
a and ε
t
≡
ε, then the convergence in distribution is obvious. It happens that the spectral gap λ appears naturally in our study. Heuristically, when the time is of order e
ε
-
2
λ, the process is very close to the probability measure
(20)
Π
t
,
ε
(
d
x
)
:
=
1
π
(
t
,
ε
)
e
-
2
ε
-
2
V
t
(
x
)
d
x
.
It remains to show the convergence of Π
t
,
ε when t goes to the infinity.LetL
t
,
ε be the operator defined by L
t
,
ε
:
=
(
1
/
2
)
ε
2
Δ
-
(
∇
V
t
,
∇
). As |
∇
V
t
|
2
-
Δ
V
t goes to the infinity as |
x
|
→
∞, the theory of Schrödinger operator (see, e.g., [13, Theorem 13.6]) implies that L
t
,
ε is self-adjoint in L
2
(
Π
t
,
ε
) and the spectrum of L
t
,
ε is discrete: 0
=
λ
1
(
t
,
ε
)
<
-
λ
2
(
t
,
ε
)
<
⋯. The subspace corresponding to the first eigenvalue λ
1
(
t
,
ε
) is composed of the constant functions and so
(21)
λ
2
(
t
,
ε
)
=
inf
⁡
⁡
{
∫
|
∇
ϕ
|
2
d
Π
t
,
ε
;
Var
⁡
Π
t
,
ε
(
ϕ
)
=
1
,
ϕ
∈
𝒟
(
ℝ
d
)
}
.
Our first aim is to compute the eigenvalue λ
2 and study its behaviour when t
→
∞.Lemma 7.
Letε
>
0 be fixed. The probability measure Π
t
,
ε converges weakly, as t
→
∞, to Π
∞
,
ε
(
d
x
)
:
=
(
1
/
π
(
ε
)
)
e
-
2
ε
-
2
V
(
x
)
d
x. Moreover, lim
⁡
ε
→
0
Π
∞
,
ε exists and is denoted by Π
0.Proof.
We only need to recall thatε
t
2
a
(
t
)
=
G
-
1
(
t
) diverges with t. More explicitly, the normalization constant is
(22)
π
(
t
,
ε
)
=
∫
ℝ
d
e
-
2
ε
-
2
V
(
x
)
e
-
2
(
|
x
|
2
/
a
(
t
)
ε
2
)
d
x
.
Let K be the compact set K
:
=
{
x
∣
V
(
x
)
≤
1
}. There exists a constant A
>
0 such that K is included in the ball centered in 0 and with radius A. Then, on one hand, we get
(23)
∫
K
c
e
-
2
ε
-
2
V
(
x
)
e
-
2
(
|
x
|
2
/
a
(
t
)
ε
2
)
d
x
≤
∫
ℝ
d
e
-
2
ε
-
2
e
-
2
(
|
x
|
2
/
a
(
t
)
ε
2
)
d
x
≤
C
(
a
(
t
)
ε
2
)
d
/
2
e
-
2
ε
-
2
.
On the other hand we obtain
(24)
∫
K
e
-
2
ε
-
2
V
(
x
)
d
x
≥
∫
K
e
-
2
ε
-
2
V
(
x
)
e
-
2
(
|
x
|
2
/
a
(
t
)
ε
2
)
d
x
≥
∫
K
‍
e
-
2
ε
-
2
V
(
x
)
e
-
2
(
A
2
/
a
(
t
)
ε
2
)
d
x
.
But we know by the Laplace formula (see [14]) that
(25)
∫
K
e
-
2
ε
2
V
(
x
)
d
x
~
t
→
+
∞
∑
i
(
2
π
ε
2
)
d
/
2
(
det
⁡
⁡
∇
2
V
(
x
i
)
)
-
1
/
2
,
where (
x
i
)
i are the global minima of V (we recall that they form a finite set). As a consequence,
(26)
π
(
t
,
ε
)
~
t
→
+
∞
∑
i
(
2
π
ε
2
)
d
/
2
(
det
⁡
∇
2
V
(
x
i
)
)
-
1
/
2
.
By the same method, if ϕ is a continuous function with compact support containing, for example, only the global minimum x
1, we have
(27)
∫
ℝ
d
ϕ
(
x
)
e
-
2
ε
-
2
V
(
x
)
e
-
2
(
|
x
|
2
/
a
(
t
)
ε
2
)
d
x
~
t
→
+
∞
(
2
π
ε
2
)
d
/
2
(
det
⁡
⁡
∇
2
V
(
x
1
)
)
-
1
/
2
ϕ
(
x
1
)
.
This gives the explicit form of lim
⁡
ε
→
0
Π
∞
,
ε
(
d
x
)
=
Π
0
(
d
x
).Consider for a momentΠ
∞
,
ε. We remark that V
t converges to V when t goes to infinity. Hwang established in [14] that Π
∞
,
ε converges weakly when ε converges to zero. Let N be the set of the global minima of V. Hwang has proved the following:(i)
ifλ
(
N
)
>
0 (where λ is the Lebesgue measure on ℝ
d), then Π
∞
,
ε converges weakly to (
1
/
λ
(
N
)
)
1
N
d
x;
(ii)
ifN
=
{
x
1
,
…
,
x
n
} then Π
∞
,
ε converges weakly to
(28)
1
∑
‍
1
≤
i
≤
n
(
det
⁡
⁡
∇
2
V
(
x
i
)
)
-
1
/
2
∑
1
≤
i
≤
n
(
det
⁡
⁡
∇
2
V
(
x
i
)
)
-
1
/
2
δ
x
i
;
(iii)
more generally, suppose thatN is the finite union of some smooth manifolds (𝒞
3) and each component is a compact connected smooth manifold and the determinant of the Hessian (normal to N in x
∈
N) det
⁡
⁡
(
∇
2
V
(
x
)
) is not identically zero. Then, there exists a probability measure ℳ, on the highest dimensional manifolds, such that Π
∞
,
ε converges weakly to (
1
/
∫
(
det
⁡
⁡
∇
2
V
(
x
)
)
-
1
/
2
ℳ
(
d
x
)
)
(
det
⁡
⁡
∇
2
V
(
x
)
)
-
1
/
2
ℳ
(
d
x
).We adapt to our setting the results of Hwang in the following proposition.Proposition 8.
The probability measureΠ
t
,
ε
t converges weakly to Π
0 as t goes to infinity. Moreover, the probability measure Π
0 concentrates on the global minima of V.Proof.
The result of Hwang shows that the probability measureΠ
∞
,
ε
t converges weakly to Π
0 as t goes to the infinity, and the probability measure Π
0 concentrates on the global minima of V. We combine this result with Lemma 7 to prove the proposition.In order to show thatZ converges in distribution to a measure supported on the global minima of V, we need two more technical results. We mix the approaches initiated by Holley et al. [6] and Miclo [7]. Indeed, we will use some functional inequalities and show that the free energy (corresponding to our process) decreases. We suppose in the following that g
∘
G
-
1
(
t
)
=
log
⁡
⁡
t
/
k for some k sufficiently large (and the same proof actually reads when g
∘
G
-
1
(
t
) is asymptotically equivalent to log
⁡
⁡
t
/
k).Definition 9.
The maximal height of the functionV
t is the nonnegative function m
(
t
) defined by
(29)
m
(
t
)
≔
sup
⁡
⁡
{
H
t
(
x
)
-
V
t
(
x
)
;
x
∈
K
}
,
where
(30)
H
t
(
x
)
≔
inf
⁡
⁡
{
E
t
(
γ
)
;
γ
∈
𝒞
1
(
[
0,1
]
,
K
)
;
γ
(
0
)
=
x
,
γ
(
1
)
=
0
}
,
E
t
(
γ
)
≔
sup
⁡
⁡
{
V
t
(
γ
(
u
)
)
;
u
∈
[
0,1
]
}
.Remark 10.
(
1
) The function m
(
t
) corresponds to the maximum of all the minimal energies needed to go from each point of ℝ
d to 0.
(
2
) The function m
(
t
) is positive if and only if there exist more than one local minimum of V.Lemma 11.
We have thatlim
⁡
t
→
∞
m
(
t
)
=
m, where m is the maximal height function corresponding to V.Proof.
LetM
:
=
sup
⁡
⁡
{
|
x
|
2
;
V
(
x
)
≤
1
}. For any path γ, we easily have E
t
(
γ
)
≤
E
∞
(
γ
)
+
M
/
a
(
t
). Then, by definition of H
t, we get
(31)
|
H
t
(
x
)
-
H
∞
(
x
)
|
≤
M
a
(
t
)
.
As a consequence, there exists C
>
0 such that
(32)
|
m
(
t
)
-
m
|
≤
sup
⁡
⁡
{
|
H
t
(
x
)
-
H
∞
(
x
)
-
|
x
|
2
a
(
t
)
|
;
x
∈
K
}
≤
C
a
(
t
)
and the result follows.A very important theorem permits one to relate the height function to the second eigenvalue of the infinitesimal generator ofY
ε (i.e., the constant involved in the spectral gap inequality).Theorem 12 (Jacquot [15, Theorem 1.1]).
The invariant measureΠ
t
,
ε admits a spectral gap λ
2, meaning that there exist C
1
,
C
2
,
ε
0
>
0 such that for all ε
>
ε
0, one has for all continuous f
∈
L
2
(
Π
t
,
ε
)
(33)
∥
P
s
t
,
ε
f
-
Π
t
,
ε
f
∥
L
2
(
Π
t
,
ε
)
≤
e
-
(
2
ε
-
2
m
(
t
)
+
log
⁡
⁡
Q
(
ε
)
-
log
⁡
⁡
(
2
-
ε
2
)
)
s
Var
⁡
Π
t
,
ε
(
f
)
=
e
-
2
λ
2
(
t
,
ε
)
Var
⁡
Π
t
,
ε
(
f
)
,
where Q
(
ε
)
=
C
W
ε
2
+
C
1
ε
-
6
d
(
1
+
C
2
ε
-
2
d
+
2
)
+
ε
2
/
(
C
W
-
1
-
(
d
-
1
)
ε
2
). Moreover, lim
⁡
ε
→
0
ε
2
log
⁡
⁡
λ
2
(
∞
,
ε
)
=
-
2
m.Corollary 13.
The family of probability measures(
Π
t
,
ε
t
,
t
≥
0
) satisfies a logarithmic Sobolev inequality L
S
I
(
C
(
t
)
), with C
(
t
)
=
Q
(
ε
t
)
e
2
ε
t
-
2
m
(
t
).Proof.
Hölder’s inequality implies that the logarithmic Sobolev constant is smaller than the inverse of the spectral gap constant in Theorem12.We will now use some functional inequalities in order to prove the convergence ofZ
t (and thus Y
t) towards the global minima of V. Let p
(
s
,
x
,
t
,
y
) denote the density of the semigroup corresponding to the nonhomogeneous Markov process Z.Theorem 14.
Suppose thatε
t
2
=
k
/
log
⁡
t, where k
>
2
m. Then, for all initial t
0
,
x
0, the free energy H
(
p
(
t
0
,
x
0
,
t
,
·
)
∣
Π
t
,
ε
t
) converges to 0 as t goes to the infinity.To prove Theorem14, we need the three following technical results. We will first state them all, postponing there proofs, and deduce from them the latter Theorem 14. Let us state the first technical result.Proposition 15.
For all initialt
0, x
0, we get
(34)
d
d
t
H
(
p
(
t
0
,
x
0
,
t
,
·
)
∣
Π
t
,
ε
t
)
≤
-
2
C
(
t
)
ε
t
2
H
(
p
(
t
0
,
x
0
,
t
,
·
)
∣
Π
t
,
ε
t
)
-
4
ε
˙
(
t
)
ε
t
-
3
∫
p
(
t
0
,
x
0
,
t
,
·
)
(
V
t
-
〈
V
t
〉
Π
t
,
ε
t
)
d
λ
‍
+
2
ε
t
2
∫
p
(
t
0
,
x
0
,
t
,
·
)
(
V
˙
t
-
〈
V
˙
t
〉
Π
t
,
ε
t
)
d
λ
,
where we have denoted V
˙
t
=
(
∂
/
∂
t
)
V
t.Lemma 16 (Miclo, [7, Lemma 6]).
Letf
:
[
0
,
∞
[
→
ℝ
+ be a continuous function such that almost surely
(35)
f
′
(
t
)
≤
α
t
-
β
t
f
(
t
)
,
where α and β are two continuous nonnegative functions such that ∫
1
∞
β
t
d
t
=
∞ and lim
⁡
t
→
∞
α
t
/
β
t
=
0. Then lim
⁡
t
→
∞
f
(
t
)
=
0.We now need a technical lemma to conclude that the free energy converges to 0.Lemma 17.
For allt
≥
0, the quantity 〈
|
x
|
2
〉
Π
t
,
ε
t is bounded.We are now ready to prove Theorem14.Proof of Theorem14.
Lett
0
≥
0 and x
0
∈
ℝ
d. Consider the process Z
t, solution to the SDE
(36)
d
Z
t
=
ε
t
d
W
t
-
(
∇
V
(
Z
t
)
+
Z
t
a
(
t
)
)
d
t
,
Z
t
0
=
x
0
.
We can rewrite the result of Proposition 15 in the following way, where we remind that p
t
=
p
(
t
0
,
x
0
,
t
,
·
) denotes the distribution law of the process Z conditioned on Z
t
0
=
x
0
(37)
d
d
t
H
(
p
t
∣
Π
t
,
ε
t
)
≤
-
2
C
(
t
)
ε
t
2
H
(
p
t
∣
Π
t
,
ε
t
)
+
2
(
ε
t
2
)
(
𝔼
V
˙
t
(
Z
t
)
-
〈
V
˙
t
〉
Π
t
,
ε
t
)
-
4
ε
˙
t
ε
t
-
3
(
𝔼
V
t
(
Z
t
)
-
〈
V
t
〉
Π
t
,
ε
t
)
.
We remind the reader that V
(
x
)
≥
c
|
x
|
2 out of a compact set and it is proved in [1] that 𝔼
V
(
Z
t
)
=
O
(
1
). We therefore have 𝔼
V
t
(
Z
t
)
=
O
(
1
). Moreover, the function t
↦
a
(
t
) is nondecreasing, while t
↦
ε
t is nonincreasing. Thus, as V
˙
t
(
x
)
=
-
(
a
˙
(
t
)
/
2
a
(
t
)
2
)
|
x
|
2, the two terms 𝔼
(
V
˙
t
(
Z
t
)
) and 〈
V
t
〉
Π
t
,
ε
t do not play any role in the upper bound. It now remains to find a upper bound for 〈
V
˙
t
〉
Π
t
,
ε
t. To this aim, we use Lemma 17. Indeed, there exist two positive constants M
1
,
M
2 such that
(38)
d
d
t
H
(
p
t
∣
Π
t
,
ε
t
)
≤


-
2
C
(
t
)
ε
t
2
H
(
p
t
∣
Π
t
,
ε
t
)


-
M
1
ε
˙
(
t
)
ε
t
3
+
M
2
a
˙
(
t
)
ε
t
2
a
(
t
)
2
.
We now use Lemma 16. We easily compute the time-derivative of a
(
t
):
(39)
a
˙
(
t
)
a
(
t
)
2
ε
t
4
=
-
ε
˙
(
t
)
ε
t
3
G
-
1
(
t
)
+
1
(
g
∘
G
-
1
(
t
)
)
G
-
1
(
t
)
2
ε
t
2
.
Using the explicit expression of ε
t; that is ε
t
2
=
k
/
log
⁡
t, we have
(40)
C
(
t
)
a
˙
(
t
)
a
(
t
)
2
ε
t
4
=
C
(
t
)
2
k
t
G
-
1
(
t
)
+
C
(
t
)
log
⁡
⁡
t
k
(
g
∘
G
-
1
(
t
)
)
G
-
1
(
t
)
2
.
As G
-
1
(
t
) is a nondecreasing function and because of the hypothesis on k, the first term converges to 0 when t goes to the infinity. For the second term, we recall that log
⁡
G
(
t
)
/
g
(
t
) is bounded and so
(41)
G
(
t
)
2
m
(
t
)
/
k
log
⁡
⁡
G
(
t
)
g
(
t
)
t
2
⟶
t
→
∞
0
,
because G
(
t
)
=
o
(
t
2
). Lemma 16 asserts that if ε satisfies ∫
‍
ε
t
2
(
d
t
/
C
(
t
)
)
=
∞ and (
ε
˙
(
t
)
/
ε
5
(
t
)
)
→
t
→
∞
0, then lim
⁡
t
→
∞
H
(
p
t
∣
Π
t
,
ε
t
)
=
0. For ε
t
2
=
k
/
log
⁡
t with the given condition on the constant k, we meet the required conditions and the result follows.Let us now prove Proposition15 and Lemma 17.Proof of Proposition15.
To shorten notation, letp
t
:
=
p
(
t
0
,
x
0
,
t
,
·
) be the distribution law of the process Z
t, knowing that Z
t
0
=
x
0. We recall that the family of probability measures (
Π
t
,
ε
t
,
t
≥
0
) satisfies a logarithmic Sobolev inequality LSI
(
C
(
t
)
). We also have Π
t
,
ε
t
(
d
x
)
=
π
t
,
ε
t
(
x
)
λ
(
d
x
). Define h
t, such that ∫
h
t
2
d
Π
t
,
ε
t
=
1:
(42)
h
t
=
p
t
π
t
,
ε
t
.
By Corollary 13, there exists a constant C
(
t
) such that
(43)
H
(
p
t
∣
Π
t
,
ε
t
)
=
∫
p
t
log
⁡
⁡
p
t
π
t
,
ε
t
d
λ
≤
C
(
t
)
∫
‍
|
∇
h
t
|
2
d
Π
t
,
ε
t
.
We now have to compute the derivative of h
t:
(44)
∇
h
t
=
1
2
p
t
π
t
,
ε
t
(
∇
p
t
p
t
+
2
∇
V
t
ε
t
2
)
.
We put this last estimate in the preceding inequality (43) and thus
(45)
H
(
p
t
∣
Π
t
,
ε
t
)
≤
C
(
t
)
4
∫
p
t
|
∇
p
t
p
t
+
2
∇
V
t
ε
t
2
|
2
d
λ
.
We recall that we are looking for an inequality including the time-derivative of the free energy H. We have
(46)
d
d
t
H
(
p
t
∣
Π
t
,
ε
t
)
=
∫
p
˙
t
log
⁡
⁡
p
t
π
t
,
ε
t
d
λ
-
∫
p
t
π
˙
t
,
ε
t
π
t
,
ε
t
d
λ
.
Our strategy is to find an upper bound for the two terms on the right-hand side. The Kolmogorov forward equation reads
(47)
p
˙
t
=
1
2
ε
t
2
Δ
p
t
+
Div
(
p
t
∇
V
t
)
=
∇
·
(
1
2
ε
t
2
∇
p
t
+
p
t
∇
V
t
)
.
We also remark that we have the following estimates:
(48)
π
˙
t
,
ε
t
π
t
,
ε
t
=
4
ε
˙
(
t
)
ε
t
3
(
V
t
-
〈
V
t
〉
π
t
,
ε
t
)
-
2
ε
t
2
(
V
˙
t
-
〈
V
˙
t
〉
Π
t
,
ε
t
)
,
where we have used the usual notation 〈
f
〉
Π
t
,
ε
t
=
∫
f
d
Π
t
,
ε
t. Moreover, we also find
(49)
∇
π
t
,
ε
t
π
t
,
ε
t
=
-
2
∇
V
t
ε
t
2
.
Now put the first estimate (48), as well as the Kolmogorov equation (47), in the formula (46). We integrate by parts and use the logarithmic Sobolev inequality (43) to get
(50)
∫
log
⁡
p
t
π
t
,
ε
t
p
˙
t
d
λ
=
∫
log
⁡
⁡
‍
p
t
π
t
,
ε
t
∇
·
(
1
2
ε
t
2
∇
p
t
+
p
t
∇
V
t
)
d
λ
=
-
∫
(
∇
p
t
p
t
-
∇
π
t
,
ε
t
π
t
,
ε
t
)
(
1
2
ε
t
2
∇
p
t
+
p
t
∇
V
t
)
d
λ
=
-
∫
(
∇
p
t
p
t
+
2
∇
V
t
ε
t
2
,
1
2
ε
t
2
∇
p
t
+
p
t
∇
V
t
)
d
λ
=
-
ε
t
2
2
∫
p
t
|
∇
p
t
p
t
+
2
∇
V
t
ε
t
2
|
2
d
λ
≤
-
2
C
(
t
)
ε
t
2
H
(
p
t
∣
Π
t
,
ε
t
)
.
On the other hand, we obtain the following equality for the second integral involved in the time-derivative of H:
(51)
∫
p
t
π
˙
t
,
ε
t
π
t
,
ε
t
d
λ
=
4
ε
˙
(
t
)
ε
t
3
∫
p
t
(
V
t
-
〈
V
t
〉
Π
t
,
ε
t
)
d
λ
-
2
ε
t
2
∫
p
t
(
V
˙
t
-
〈
V
˙
t
〉
Π
t
,
ε
t
)
d
λ
.
We put all the pieces together and this leads to the result.Proof of Lemma17.
LetK be the compact set K
:
=
{
x
;
V
(
x
)
≤
η
}, where η is a given positive constant. As Π
t
,
ε
t converges weakly to Π
0, we only need to prove that 〈
|
x
|
2
1
K
c
〉
Π
t
,
ε
t is bounded. We have
(52)
∫
K
c
|
x
|
2
e
-
2
ε
t
-
2
V
(
x
)
e
-
2
(
|
x
|
2
/
a
(
t
)
ε
t
2
)
d
x
≤
∫
K
c
|
x
|
2
e
-
2
V
(
x
)
e
-
2
V
(
x
)
(
ε
t
-
2
-
1
)
d
x
≤
∫
K
c
|
x
|
2
e
-
2
V
(
x
)
d
x
e
-
2
η
(
ε
t
-
2
-
1
)
.
By Proposition 8, we know that π
(
t
,
ε
t
)
~
t
→
+
∞
∑
i
‍
(
2
π
ε
t
2
)
d
/
2
(
det
⁡
∇
2
V
(
x
i
)
)
-
1
/
2, and so there exists a positive constant C
~ such that
(53)
〈
|
x
|
2
1
K
c
〉
Π
t
,
ε
t
≤
C
~
ε
t
-
d
e
-
2
η
ε
t
-
2
⟶
t
→
+
∞
0
.We will now describe the law of the limit processY
∞.Proposition 18.
The speed of convergence ofH
(
p
(
t
0
,
x
0
,
t
,
·
)
∣
Π
t
,
ε
t
) toward 0 is 1
/
(
G
-
1
(
t
)
log
⁡
⁡
t
).Proof.
By Lemma16, the speed of convergence is given by ∫
0
t
‍
α
s
e
-
∫
s
t
β
u
d
u
d
s, with β
s
=
s
2
m
/
k
/
log
⁡
⁡
s and α
s
=
(
s
G
-
1
(
s
)
)
-
1
+
log
⁡
⁡
s
(
g
∘
G
-
1
(
s
)
G
-
1
(
s
)
2
)
-
1. Integrating by part, we find that ∫
0
t
‍
β
s
d
s is equivalent, when t goes to the infinity and to t
1
+
2
m
/
k
/
log
⁡
⁡
t and thus the speed of convergence is of order (
G
-
1
(
t
)
log
⁡
⁡
t
)
-
1
+
(
g
∘
G
-
1
(
t
)
(
G
-
1
(
t
)
)
2
)
-
1. Finally, the speed of convergence of the relative Kullback information to zero is (
G
-
1
(
t
)
log
⁡
⁡
t
)
-
1
=
o
(
(
log
⁡
⁡
t
)
-
1
).Remark 19.
It is known since the work of Freidlin and Wentzell [2] that the Gibbs measure Π
t
,
ε
t satisfies a large deviation principle. Therefore, the speed of convergence of Π
t
,
ε
t toward Π
0 is exponential (e
-
log
⁡
⁡
t
/
2
k
=
t
-
1
/
2
k).Corollary 20.
Suppose thatε
t
2
=
k
/
log
⁡
⁡
t, where k
>
2
m. Then the process Z converges in distribution to a random variable which concentrates on the global minima of V. Thus, the process Y converges in distribution to a random variable Y
∞, which concentrates on the global minima of V.Proof.
The Kullback informationH
(
p
t
∣
Π
t
,
ε
t
) estimates the distance between p
t and Π
t
,
ε
t, as it is recalled in (15). The result follows as Π
t
,
ε
t converges weakly to Π
0.Remark 21.
The functionε is supposed to decrease slowly to zero. This is why we obtain the convergence of Y to the global minima of V. But if ε goes too fast to zero, that is, lim
⁡
t
→
∞
g
(
t
)
-
1
log
⁡
⁡
G
(
t
)
=
k with k
≤
2
m, then Y may freeze in a local minimum. So, X does not converge in that case.


### 3.2. Study ofX

We give necessary and sufficient conditions for the convergence in distribution ofX. As usual, we start to work with the process Y
t
=
X
t
-
μ
¯
t. In order to link this section with the preceding one, we recall that ε
t
2
=
(
g
∘
G
-
1
(
t
)
)
-
1
=
k
/
log
⁡
⁡
t. It implies that we consider functions g such that (asymptotically) log
⁡
⁡
G
(
t
)
=
k
g
(
t
).Let us first recall a former result.Theorem 22 (Chambeu and Kurtzmann [1, Theorem 5.5]).
The processY satisfies the pointwise ergodic theorem. This means that almost surely the empirical measure of Y converges weakly to a random measure, which is a convex combination of Dirac measures taken in the minimal points of V. More precisely, there exist a
i
≥
0 such that
(54)
1
t
∫
0
t
δ
Y
s
d
s
⟶
t
→
∞
∑
i
=
1
n
a
i
δ
m
i
a
.
s
.We are now able to conclude the study of the asymptotic behaviour of the processX.Theorem 23.
Suppose thatlim
⁡
t
→
∞
g
(
t
)
-
1
log
⁡
⁡
G
(
t
)
=
k
>
2
m. Then one of the following holds.(1)
IfV is a function such that ∑
‍
1
≤
i
≤
n
a
i
m
i
=
0, then X
t converges in distribution to Y
∞
+
∫
0
∞
Y
s
(
d
s
/
s
);
(2)
else,X
t diverges.Proof.
Suppose thatV is such that the integral ∫
0
t
Y
s
(
d
s
/
s
) converges almost surely. The celebrated Slutsky theorem asserts that for two sequences (
U
t
), (
W
t
) of ℝ
d valued random variables, if U
t
→
(
d
)
t
→
∞
U and |
U
t
-
W
t
|
→
ℙ
t
→
∞
0, then W
t
→
(
d
)
t
→
∞
U. To prove the result, we let U
t
=
μ
¯
t
=
∫
0
t
(
d
s
/
s
)
Y
s
=
(
1
/
t
)
∫
0
t
Y
s
d
s
+
∫
0
t
(
1
/
s
2
)
∫
0
s
Y
u
d
u
d
s, W
t
=
X
t, and remark that
(55)
X
t
=
Y
t
+
∫
0
t
‍
d
s
s
Y
s
=
Y
t
+
μ
¯
t
.
Suppose that V is such that ∑
‍
1
≤
i
≤
n
a
i
m
i
=
0. By Theorem 22, we have that (
1
/
t
)
∫
0
t
‍
Y
s
d
s
→
a
.
s
.
0, and we now need to find the rate of convergence in order to conclude the proof. Moreover, by [1, Proposition 5.3], we know that the speed of convergence of the empirical mean of the time-changed process Y
G
-
1
(
t
) is G
-
1
(
1
+
t
)
-
G
-
1
(
t
). But we are looking for the speed of convergence for Y
t itself. By an integration by part, we obtain that
(56)
1
t
∫
0
t
Y
s
d
s
‍
=
1
t
∫
0
G
(
t
)
Y
G
-
1
(
u
)
d
u
g
∘
G
-
1
(
u
)
=
1
t
g
(
t
)
∫
0
G
(
t
)
Y
G
-
1
(
u
)
d
u
+
1
t
‍
∫
0
G
(
t
)
d
u
g
′
∘
G
-
1
(
u
)
(
g
∘
G
-
1
(
u
)
)
3
‍
∫
0
u
Y
G
-
1
(
s
)
d
s
.
Corollary 20 implies that the first right-hand term converges in distribution to 0 because G
(
t
)
≤
t
g
(
t
). So it converges in probability to 0. It remains to prove the convergence of the second term. We recall that, up to a multiplicative positive constant, g
∘
G
-
1
(
u
)
=
log
⁡
⁡
(
2
+
u
). Moreover, we also know that (
1
/
u
)
∫
0
u
‍
Y
G
-
1
(
s
)
d
s is almost surely bounded. So, the second right-hand term is upper bounded (up to a multiplicative positive constant) by
(57)
1
t
∫
0
G
(
t
)
d
u
(
log
⁡
⁡
(
2
+
u
)
)
2
=
G
(
t
)
t
(
log
⁡
⁡
G
(
t
)
)
2
+
o
(
G
(
t
)
t
(
log
⁡
⁡
G
(
t
)
)
2
)
≤
g
(
t
)
log
⁡
⁡
G
(
t
)
1
log
⁡
⁡
G
(
t
)
and the result is as follows: μ
¯
t
=
U
t converges in distribution. And by Corollary 20, Y
t converges in distribution to Y
∞ which concentrates on the global minima of V. So, Y
t
=
U
t
-
W
t converges in probability to 0.
To conclude, ifV satisfies ∑
‍
1
≤
i
≤
n
a
i
m
i
≠
0, then μ
¯
t
=
∫
0
t
‍
Y
s
(
d
s
/
s
) does not converge and so X
t diverges.


## 3.1. Convergence in Distribution towards the Global Minima ofV

We defineε
t
2
=
1
/
(
g
∘
G
-
1
(
t
)
) and a
(
t
)
=
G
-
1
(
t
)
g
∘
G
-
1
(
t
). The process Z satisfies
(19)
d
Z
t
=
ε
t
d
B
t
-
∇
V
t
(
Z
t
)
d
t
,
where we have defined V
t
(
x
)
:
=
V
(
x
)
+
|
x
|
2
/
2
a
(
t
). Actually, we will prove that this nonhomogeneous Markov process converges in distribution to a measure that could correspond to its “invariant” probability measure. Of course, if we suppose that a
(
t
)
≡
a and ε
t
≡
ε, then the convergence in distribution is obvious. It happens that the spectral gap λ appears naturally in our study. Heuristically, when the time is of order e
ε
-
2
λ, the process is very close to the probability measure
(20)
Π
t
,
ε
(
d
x
)
:
=
1
π
(
t
,
ε
)
e
-
2
ε
-
2
V
t
(
x
)
d
x
.
It remains to show the convergence of Π
t
,
ε when t goes to the infinity.LetL
t
,
ε be the operator defined by L
t
,
ε
:
=
(
1
/
2
)
ε
2
Δ
-
(
∇
V
t
,
∇
). As |
∇
V
t
|
2
-
Δ
V
t goes to the infinity as |
x
|
→
∞, the theory of Schrödinger operator (see, e.g., [13, Theorem 13.6]) implies that L
t
,
ε is self-adjoint in L
2
(
Π
t
,
ε
) and the spectrum of L
t
,
ε is discrete: 0
=
λ
1
(
t
,
ε
)
<
-
λ
2
(
t
,
ε
)
<
⋯. The subspace corresponding to the first eigenvalue λ
1
(
t
,
ε
) is composed of the constant functions and so
(21)
λ
2
(
t
,
ε
)
=
inf
⁡
⁡
{
∫
|
∇
ϕ
|
2
d
Π
t
,
ε
;
Var
⁡
Π
t
,
ε
(
ϕ
)
=
1
,
ϕ
∈
𝒟
(
ℝ
d
)
}
.
Our first aim is to compute the eigenvalue λ
2 and study its behaviour when t
→
∞.Lemma 7.
Letε
>
0 be fixed. The probability measure Π
t
,
ε converges weakly, as t
→
∞, to Π
∞
,
ε
(
d
x
)
:
=
(
1
/
π
(
ε
)
)
e
-
2
ε
-
2
V
(
x
)
d
x. Moreover, lim
⁡
ε
→
0
Π
∞
,
ε exists and is denoted by Π
0.Proof.
We only need to recall thatε
t
2
a
(
t
)
=
G
-
1
(
t
) diverges with t. More explicitly, the normalization constant is
(22)
π
(
t
,
ε
)
=
∫
ℝ
d
e
-
2
ε
-
2
V
(
x
)
e
-
2
(
|
x
|
2
/
a
(
t
)
ε
2
)
d
x
.
Let K be the compact set K
:
=
{
x
∣
V
(
x
)
≤
1
}. There exists a constant A
>
0 such that K is included in the ball centered in 0 and with radius A. Then, on one hand, we get
(23)
∫
K
c
e
-
2
ε
-
2
V
(
x
)
e
-
2
(
|
x
|
2
/
a
(
t
)
ε
2
)
d
x
≤
∫
ℝ
d
e
-
2
ε
-
2
e
-
2
(
|
x
|
2
/
a
(
t
)
ε
2
)
d
x
≤
C
(
a
(
t
)
ε
2
)
d
/
2
e
-
2
ε
-
2
.
On the other hand we obtain
(24)
∫
K
e
-
2
ε
-
2
V
(
x
)
d
x
≥
∫
K
e
-
2
ε
-
2
V
(
x
)
e
-
2
(
|
x
|
2
/
a
(
t
)
ε
2
)
d
x
≥
∫
K
‍
e
-
2
ε
-
2
V
(
x
)
e
-
2
(
A
2
/
a
(
t
)
ε
2
)
d
x
.
But we know by the Laplace formula (see [14]) that
(25)
∫
K
e
-
2
ε
2
V
(
x
)
d
x
~
t
→
+
∞
∑
i
(
2
π
ε
2
)
d
/
2
(
det
⁡
⁡
∇
2
V
(
x
i
)
)
-
1
/
2
,
where (
x
i
)
i are the global minima of V (we recall that they form a finite set). As a consequence,
(26)
π
(
t
,
ε
)
~
t
→
+
∞
∑
i
(
2
π
ε
2
)
d
/
2
(
det
⁡
∇
2
V
(
x
i
)
)
-
1
/
2
.
By the same method, if ϕ is a continuous function with compact support containing, for example, only the global minimum x
1, we have
(27)
∫
ℝ
d
ϕ
(
x
)
e
-
2
ε
-
2
V
(
x
)
e
-
2
(
|
x
|
2
/
a
(
t
)
ε
2
)
d
x
~
t
→
+
∞
(
2
π
ε
2
)
d
/
2
(
det
⁡
⁡
∇
2
V
(
x
1
)
)
-
1
/
2
ϕ
(
x
1
)
.
This gives the explicit form of lim
⁡
ε
→
0
Π
∞
,
ε
(
d
x
)
=
Π
0
(
d
x
).Consider for a momentΠ
∞
,
ε. We remark that V
t converges to V when t goes to infinity. Hwang established in [14] that Π
∞
,
ε converges weakly when ε converges to zero. Let N be the set of the global minima of V. Hwang has proved the following:(i)
ifλ
(
N
)
>
0 (where λ is the Lebesgue measure on ℝ
d), then Π
∞
,
ε converges weakly to (
1
/
λ
(
N
)
)
1
N
d
x;
(ii)
ifN
=
{
x
1
,
…
,
x
n
} then Π
∞
,
ε converges weakly to
(28)
1
∑
‍
1
≤
i
≤
n
(
det
⁡
⁡
∇
2
V
(
x
i
)
)
-
1
/
2
∑
1
≤
i
≤
n
(
det
⁡
⁡
∇
2
V
(
x
i
)
)
-
1
/
2
δ
x
i
;
(iii)
more generally, suppose thatN is the finite union of some smooth manifolds (𝒞
3) and each component is a compact connected smooth manifold and the determinant of the Hessian (normal to N in x
∈
N) det
⁡
⁡
(
∇
2
V
(
x
)
) is not identically zero. Then, there exists a probability measure ℳ, on the highest dimensional manifolds, such that Π
∞
,
ε converges weakly to (
1
/
∫
(
det
⁡
⁡
∇
2
V
(
x
)
)
-
1
/
2
ℳ
(
d
x
)
)
(
det
⁡
⁡
∇
2
V
(
x
)
)
-
1
/
2
ℳ
(
d
x
).We adapt to our setting the results of Hwang in the following proposition.Proposition 8.
The probability measureΠ
t
,
ε
t converges weakly to Π
0 as t goes to infinity. Moreover, the probability measure Π
0 concentrates on the global minima of V.Proof.
The result of Hwang shows that the probability measureΠ
∞
,
ε
t converges weakly to Π
0 as t goes to the infinity, and the probability measure Π
0 concentrates on the global minima of V. We combine this result with Lemma 7 to prove the proposition.In order to show thatZ converges in distribution to a measure supported on the global minima of V, we need two more technical results. We mix the approaches initiated by Holley et al. [6] and Miclo [7]. Indeed, we will use some functional inequalities and show that the free energy (corresponding to our process) decreases. We suppose in the following that g
∘
G
-
1
(
t
)
=
log
⁡
⁡
t
/
k for some k sufficiently large (and the same proof actually reads when g
∘
G
-
1
(
t
) is asymptotically equivalent to log
⁡
⁡
t
/
k).Definition 9.
The maximal height of the functionV
t is the nonnegative function m
(
t
) defined by
(29)
m
(
t
)
≔
sup
⁡
⁡
{
H
t
(
x
)
-
V
t
(
x
)
;
x
∈
K
}
,
where
(30)
H
t
(
x
)
≔
inf
⁡
⁡
{
E
t
(
γ
)
;
γ
∈
𝒞
1
(
[
0,1
]
,
K
)
;
γ
(
0
)
=
x
,
γ
(
1
)
=
0
}
,
E
t
(
γ
)
≔
sup
⁡
⁡
{
V
t
(
γ
(
u
)
)
;
u
∈
[
0,1
]
}
.Remark 10.
(
1
) The function m
(
t
) corresponds to the maximum of all the minimal energies needed to go from each point of ℝ
d to 0.
(
2
) The function m
(
t
) is positive if and only if there exist more than one local minimum of V.Lemma 11.
We have thatlim
⁡
t
→
∞
m
(
t
)
=
m, where m is the maximal height function corresponding to V.Proof.
LetM
:
=
sup
⁡
⁡
{
|
x
|
2
;
V
(
x
)
≤
1
}. For any path γ, we easily have E
t
(
γ
)
≤
E
∞
(
γ
)
+
M
/
a
(
t
). Then, by definition of H
t, we get
(31)
|
H
t
(
x
)
-
H
∞
(
x
)
|
≤
M
a
(
t
)
.
As a consequence, there exists C
>
0 such that
(32)
|
m
(
t
)
-
m
|
≤
sup
⁡
⁡
{
|
H
t
(
x
)
-
H
∞
(
x
)
-
|
x
|
2
a
(
t
)
|
;
x
∈
K
}
≤
C
a
(
t
)
and the result follows.A very important theorem permits one to relate the height function to the second eigenvalue of the infinitesimal generator ofY
ε (i.e., the constant involved in the spectral gap inequality).Theorem 12 (Jacquot [15, Theorem 1.1]).
The invariant measureΠ
t
,
ε admits a spectral gap λ
2, meaning that there exist C
1
,
C
2
,
ε
0
>
0 such that for all ε
>
ε
0, one has for all continuous f
∈
L
2
(
Π
t
,
ε
)
(33)
∥
P
s
t
,
ε
f
-
Π
t
,
ε
f
∥
L
2
(
Π
t
,
ε
)
≤
e
-
(
2
ε
-
2
m
(
t
)
+
log
⁡
⁡
Q
(
ε
)
-
log
⁡
⁡
(
2
-
ε
2
)
)
s
Var
⁡
Π
t
,
ε
(
f
)
=
e
-
2
λ
2
(
t
,
ε
)
Var
⁡
Π
t
,
ε
(
f
)
,
where Q
(
ε
)
=
C
W
ε
2
+
C
1
ε
-
6
d
(
1
+
C
2
ε
-
2
d
+
2
)
+
ε
2
/
(
C
W
-
1
-
(
d
-
1
)
ε
2
). Moreover, lim
⁡
ε
→
0
ε
2
log
⁡
⁡
λ
2
(
∞
,
ε
)
=
-
2
m.Corollary 13.
The family of probability measures(
Π
t
,
ε
t
,
t
≥
0
) satisfies a logarithmic Sobolev inequality L
S
I
(
C
(
t
)
), with C
(
t
)
=
Q
(
ε
t
)
e
2
ε
t
-
2
m
(
t
).Proof.
Hölder’s inequality implies that the logarithmic Sobolev constant is smaller than the inverse of the spectral gap constant in Theorem12.We will now use some functional inequalities in order to prove the convergence ofZ
t (and thus Y
t) towards the global minima of V. Let p
(
s
,
x
,
t
,
y
) denote the density of the semigroup corresponding to the nonhomogeneous Markov process Z.Theorem 14.
Suppose thatε
t
2
=
k
/
log
⁡
t, where k
>
2
m. Then, for all initial t
0
,
x
0, the free energy H
(
p
(
t
0
,
x
0
,
t
,
·
)
∣
Π
t
,
ε
t
) converges to 0 as t goes to the infinity.To prove Theorem14, we need the three following technical results. We will first state them all, postponing there proofs, and deduce from them the latter Theorem 14. Let us state the first technical result.Proposition 15.
For all initialt
0, x
0, we get
(34)
d
d
t
H
(
p
(
t
0
,
x
0
,
t
,
·
)
∣
Π
t
,
ε
t
)
≤
-
2
C
(
t
)
ε
t
2
H
(
p
(
t
0
,
x
0
,
t
,
·
)
∣
Π
t
,
ε
t
)
-
4
ε
˙
(
t
)
ε
t
-
3
∫
p
(
t
0
,
x
0
,
t
,
·
)
(
V
t
-
〈
V
t
〉
Π
t
,
ε
t
)
d
λ
‍
+
2
ε
t
2
∫
p
(
t
0
,
x
0
,
t
,
·
)
(
V
˙
t
-
〈
V
˙
t
〉
Π
t
,
ε
t
)
d
λ
,
where we have denoted V
˙
t
=
(
∂
/
∂
t
)
V
t.Lemma 16 (Miclo, [7, Lemma 6]).
Letf
:
[
0
,
∞
[
→
ℝ
+ be a continuous function such that almost surely
(35)
f
′
(
t
)
≤
α
t
-
β
t
f
(
t
)
,
where α and β are two continuous nonnegative functions such that ∫
1
∞
β
t
d
t
=
∞ and lim
⁡
t
→
∞
α
t
/
β
t
=
0. Then lim
⁡
t
→
∞
f
(
t
)
=
0.We now need a technical lemma to conclude that the free energy converges to 0.Lemma 17.
For allt
≥
0, the quantity 〈
|
x
|
2
〉
Π
t
,
ε
t is bounded.We are now ready to prove Theorem14.Proof of Theorem14.
Lett
0
≥
0 and x
0
∈
ℝ
d. Consider the process Z
t, solution to the SDE
(36)
d
Z
t
=
ε
t
d
W
t
-
(
∇
V
(
Z
t
)
+
Z
t
a
(
t
)
)
d
t
,
Z
t
0
=
x
0
.
We can rewrite the result of Proposition 15 in the following way, where we remind that p
t
=
p
(
t
0
,
x
0
,
t
,
·
) denotes the distribution law of the process Z conditioned on Z
t
0
=
x
0
(37)
d
d
t
H
(
p
t
∣
Π
t
,
ε
t
)
≤
-
2
C
(
t
)
ε
t
2
H
(
p
t
∣
Π
t
,
ε
t
)
+
2
(
ε
t
2
)
(
𝔼
V
˙
t
(
Z
t
)
-
〈
V
˙
t
〉
Π
t
,
ε
t
)
-
4
ε
˙
t
ε
t
-
3
(
𝔼
V
t
(
Z
t
)
-
〈
V
t
〉
Π
t
,
ε
t
)
.
We remind the reader that V
(
x
)
≥
c
|
x
|
2 out of a compact set and it is proved in [1] that 𝔼
V
(
Z
t
)
=
O
(
1
). We therefore have 𝔼
V
t
(
Z
t
)
=
O
(
1
). Moreover, the function t
↦
a
(
t
) is nondecreasing, while t
↦
ε
t is nonincreasing. Thus, as V
˙
t
(
x
)
=
-
(
a
˙
(
t
)
/
2
a
(
t
)
2
)
|
x
|
2, the two terms 𝔼
(
V
˙
t
(
Z
t
)
) and 〈
V
t
〉
Π
t
,
ε
t do not play any role in the upper bound. It now remains to find a upper bound for 〈
V
˙
t
〉
Π
t
,
ε
t. To this aim, we use Lemma 17. Indeed, there exist two positive constants M
1
,
M
2 such that
(38)
d
d
t
H
(
p
t
∣
Π
t
,
ε
t
)
≤


-
2
C
(
t
)
ε
t
2
H
(
p
t
∣
Π
t
,
ε
t
)


-
M
1
ε
˙
(
t
)
ε
t
3
+
M
2
a
˙
(
t
)
ε
t
2
a
(
t
)
2
.
We now use Lemma 16. We easily compute the time-derivative of a
(
t
):
(39)
a
˙
(
t
)
a
(
t
)
2
ε
t
4
=
-
ε
˙
(
t
)
ε
t
3
G
-
1
(
t
)
+
1
(
g
∘
G
-
1
(
t
)
)
G
-
1
(
t
)
2
ε
t
2
.
Using the explicit expression of ε
t; that is ε
t
2
=
k
/
log
⁡
t, we have
(40)
C
(
t
)
a
˙
(
t
)
a
(
t
)
2
ε
t
4
=
C
(
t
)
2
k
t
G
-
1
(
t
)
+
C
(
t
)
log
⁡
⁡
t
k
(
g
∘
G
-
1
(
t
)
)
G
-
1
(
t
)
2
.
As G
-
1
(
t
) is a nondecreasing function and because of the hypothesis on k, the first term converges to 0 when t goes to the infinity. For the second term, we recall that log
⁡
G
(
t
)
/
g
(
t
) is bounded and so
(41)
G
(
t
)
2
m
(
t
)
/
k
log
⁡
⁡
G
(
t
)
g
(
t
)
t
2
⟶
t
→
∞
0
,
because G
(
t
)
=
o
(
t
2
). Lemma 16 asserts that if ε satisfies ∫
‍
ε
t
2
(
d
t
/
C
(
t
)
)
=
∞ and (
ε
˙
(
t
)
/
ε
5
(
t
)
)
→
t
→
∞
0, then lim
⁡
t
→
∞
H
(
p
t
∣
Π
t
,
ε
t
)
=
0. For ε
t
2
=
k
/
log
⁡
t with the given condition on the constant k, we meet the required conditions and the result follows.Let us now prove Proposition15 and Lemma 17.Proof of Proposition15.
To shorten notation, letp
t
:
=
p
(
t
0
,
x
0
,
t
,
·
) be the distribution law of the process Z
t, knowing that Z
t
0
=
x
0. We recall that the family of probability measures (
Π
t
,
ε
t
,
t
≥
0
) satisfies a logarithmic Sobolev inequality LSI
(
C
(
t
)
). We also have Π
t
,
ε
t
(
d
x
)
=
π
t
,
ε
t
(
x
)
λ
(
d
x
). Define h
t, such that ∫
h
t
2
d
Π
t
,
ε
t
=
1:
(42)
h
t
=
p
t
π
t
,
ε
t
.
By Corollary 13, there exists a constant C
(
t
) such that
(43)
H
(
p
t
∣
Π
t
,
ε
t
)
=
∫
p
t
log
⁡
⁡
p
t
π
t
,
ε
t
d
λ
≤
C
(
t
)
∫
‍
|
∇
h
t
|
2
d
Π
t
,
ε
t
.
We now have to compute the derivative of h
t:
(44)
∇
h
t
=
1
2
p
t
π
t
,
ε
t
(
∇
p
t
p
t
+
2
∇
V
t
ε
t
2
)
.
We put this last estimate in the preceding inequality (43) and thus
(45)
H
(
p
t
∣
Π
t
,
ε
t
)
≤
C
(
t
)
4
∫
p
t
|
∇
p
t
p
t
+
2
∇
V
t
ε
t
2
|
2
d
λ
.
We recall that we are looking for an inequality including the time-derivative of the free energy H. We have
(46)
d
d
t
H
(
p
t
∣
Π
t
,
ε
t
)
=
∫
p
˙
t
log
⁡
⁡
p
t
π
t
,
ε
t
d
λ
-
∫
p
t
π
˙
t
,
ε
t
π
t
,
ε
t
d
λ
.
Our strategy is to find an upper bound for the two terms on the right-hand side. The Kolmogorov forward equation reads
(47)
p
˙
t
=
1
2
ε
t
2
Δ
p
t
+
Div
(
p
t
∇
V
t
)
=
∇
·
(
1
2
ε
t
2
∇
p
t
+
p
t
∇
V
t
)
.
We also remark that we have the following estimates:
(48)
π
˙
t
,
ε
t
π
t
,
ε
t
=
4
ε
˙
(
t
)
ε
t
3
(
V
t
-
〈
V
t
〉
π
t
,
ε
t
)
-
2
ε
t
2
(
V
˙
t
-
〈
V
˙
t
〉
Π
t
,
ε
t
)
,
where we have used the usual notation 〈
f
〉
Π
t
,
ε
t
=
∫
f
d
Π
t
,
ε
t. Moreover, we also find
(49)
∇
π
t
,
ε
t
π
t
,
ε
t
=
-
2
∇
V
t
ε
t
2
.
Now put the first estimate (48), as well as the Kolmogorov equation (47), in the formula (46). We integrate by parts and use the logarithmic Sobolev inequality (43) to get
(50)
∫
log
⁡
p
t
π
t
,
ε
t
p
˙
t
d
λ
=
∫
log
⁡
⁡
‍
p
t
π
t
,
ε
t
∇
·
(
1
2
ε
t
2
∇
p
t
+
p
t
∇
V
t
)
d
λ
=
-
∫
(
∇
p
t
p
t
-
∇
π
t
,
ε
t
π
t
,
ε
t
)
(
1
2
ε
t
2
∇
p
t
+
p
t
∇
V
t
)
d
λ
=
-
∫
(
∇
p
t
p
t
+
2
∇
V
t
ε
t
2
,
1
2
ε
t
2
∇
p
t
+
p
t
∇
V
t
)
d
λ
=
-
ε
t
2
2
∫
p
t
|
∇
p
t
p
t
+
2
∇
V
t
ε
t
2
|
2
d
λ
≤
-
2
C
(
t
)
ε
t
2
H
(
p
t
∣
Π
t
,
ε
t
)
.
On the other hand, we obtain the following equality for the second integral involved in the time-derivative of H:
(51)
∫
p
t
π
˙
t
,
ε
t
π
t
,
ε
t
d
λ
=
4
ε
˙
(
t
)
ε
t
3
∫
p
t
(
V
t
-
〈
V
t
〉
Π
t
,
ε
t
)
d
λ
-
2
ε
t
2
∫
p
t
(
V
˙
t
-
〈
V
˙
t
〉
Π
t
,
ε
t
)
d
λ
.
We put all the pieces together and this leads to the result.Proof of Lemma17.
LetK be the compact set K
:
=
{
x
;
V
(
x
)
≤
η
}, where η is a given positive constant. As Π
t
,
ε
t converges weakly to Π
0, we only need to prove that 〈
|
x
|
2
1
K
c
〉
Π
t
,
ε
t is bounded. We have
(52)
∫
K
c
|
x
|
2
e
-
2
ε
t
-
2
V
(
x
)
e
-
2
(
|
x
|
2
/
a
(
t
)
ε
t
2
)
d
x
≤
∫
K
c
|
x
|
2
e
-
2
V
(
x
)
e
-
2
V
(
x
)
(
ε
t
-
2
-
1
)
d
x
≤
∫
K
c
|
x
|
2
e
-
2
V
(
x
)
d
x
e
-
2
η
(
ε
t
-
2
-
1
)
.
By Proposition 8, we know that π
(
t
,
ε
t
)
~
t
→
+
∞
∑
i
‍
(
2
π
ε
t
2
)
d
/
2
(
det
⁡
∇
2
V
(
x
i
)
)
-
1
/
2, and so there exists a positive constant C
~ such that
(53)
〈
|
x
|
2
1
K
c
〉
Π
t
,
ε
t
≤
C
~
ε
t
-
d
e
-
2
η
ε
t
-
2
⟶
t
→
+
∞
0
.We will now describe the law of the limit processY
∞.Proposition 18.
The speed of convergence ofH
(
p
(
t
0
,
x
0
,
t
,
·
)
∣
Π
t
,
ε
t
) toward 0 is 1
/
(
G
-
1
(
t
)
log
⁡
⁡
t
).Proof.
By Lemma16, the speed of convergence is given by ∫
0
t
‍
α
s
e
-
∫
s
t
β
u
d
u
d
s, with β
s
=
s
2
m
/
k
/
log
⁡
⁡
s and α
s
=
(
s
G
-
1
(
s
)
)
-
1
+
log
⁡
⁡
s
(
g
∘
G
-
1
(
s
)
G
-
1
(
s
)
2
)
-
1. Integrating by part, we find that ∫
0
t
‍
β
s
d
s is equivalent, when t goes to the infinity and to t
1
+
2
m
/
k
/
log
⁡
⁡
t and thus the speed of convergence is of order (
G
-
1
(
t
)
log
⁡
⁡
t
)
-
1
+
(
g
∘
G
-
1
(
t
)
(
G
-
1
(
t
)
)
2
)
-
1. Finally, the speed of convergence of the relative Kullback information to zero is (
G
-
1
(
t
)
log
⁡
⁡
t
)
-
1
=
o
(
(
log
⁡
⁡
t
)
-
1
).Remark 19.
It is known since the work of Freidlin and Wentzell [2] that the Gibbs measure Π
t
,
ε
t satisfies a large deviation principle. Therefore, the speed of convergence of Π
t
,
ε
t toward Π
0 is exponential (e
-
log
⁡
⁡
t
/
2
k
=
t
-
1
/
2
k).Corollary 20.
Suppose thatε
t
2
=
k
/
log
⁡
⁡
t, where k
>
2
m. Then the process Z converges in distribution to a random variable which concentrates on the global minima of V. Thus, the process Y converges in distribution to a random variable Y
∞, which concentrates on the global minima of V.Proof.
The Kullback informationH
(
p
t
∣
Π
t
,
ε
t
) estimates the distance between p
t and Π
t
,
ε
t, as it is recalled in (15). The result follows as Π
t
,
ε
t converges weakly to Π
0.Remark 21.
The functionε is supposed to decrease slowly to zero. This is why we obtain the convergence of Y to the global minima of V. But if ε goes too fast to zero, that is, lim
⁡
t
→
∞
g
(
t
)
-
1
log
⁡
⁡
G
(
t
)
=
k with k
≤
2
m, then Y may freeze in a local minimum. So, X does not converge in that case.


## 3.2. Study ofX

We give necessary and sufficient conditions for the convergence in distribution ofX. As usual, we start to work with the process Y
t
=
X
t
-
μ
¯
t. In order to link this section with the preceding one, we recall that ε
t
2
=
(
g
∘
G
-
1
(
t
)
)
-
1
=
k
/
log
⁡
⁡
t. It implies that we consider functions g such that (asymptotically) log
⁡
⁡
G
(
t
)
=
k
g
(
t
).Let us first recall a former result.Theorem 22 (Chambeu and Kurtzmann [1, Theorem 5.5]).
The processY satisfies the pointwise ergodic theorem. This means that almost surely the empirical measure of Y converges weakly to a random measure, which is a convex combination of Dirac measures taken in the minimal points of V. More precisely, there exist a
i
≥
0 such that
(54)
1
t
∫
0
t
δ
Y
s
d
s
⟶
t
→
∞
∑
i
=
1
n
a
i
δ
m
i
a
.
s
.We are now able to conclude the study of the asymptotic behaviour of the processX.Theorem 23.
Suppose thatlim
⁡
t
→
∞
g
(
t
)
-
1
log
⁡
⁡
G
(
t
)
=
k
>
2
m. Then one of the following holds.(1)
IfV is a function such that ∑
‍
1
≤
i
≤
n
a
i
m
i
=
0, then X
t converges in distribution to Y
∞
+
∫
0
∞
Y
s
(
d
s
/
s
);
(2)
else,X
t diverges.Proof.
Suppose thatV is such that the integral ∫
0
t
Y
s
(
d
s
/
s
) converges almost surely. The celebrated Slutsky theorem asserts that for two sequences (
U
t
), (
W
t
) of ℝ
d valued random variables, if U
t
→
(
d
)
t
→
∞
U and |
U
t
-
W
t
|
→
ℙ
t
→
∞
0, then W
t
→
(
d
)
t
→
∞
U. To prove the result, we let U
t
=
μ
¯
t
=
∫
0
t
(
d
s
/
s
)
Y
s
=
(
1
/
t
)
∫
0
t
Y
s
d
s
+
∫
0
t
(
1
/
s
2
)
∫
0
s
Y
u
d
u
d
s, W
t
=
X
t, and remark that
(55)
X
t
=
Y
t
+
∫
0
t
‍
d
s
s
Y
s
=
Y
t
+
μ
¯
t
.
Suppose that V is such that ∑
‍
1
≤
i
≤
n
a
i
m
i
=
0. By Theorem 22, we have that (
1
/
t
)
∫
0
t
‍
Y
s
d
s
→
a
.
s
.
0, and we now need to find the rate of convergence in order to conclude the proof. Moreover, by [1, Proposition 5.3], we know that the speed of convergence of the empirical mean of the time-changed process Y
G
-
1
(
t
) is G
-
1
(
1
+
t
)
-
G
-
1
(
t
). But we are looking for the speed of convergence for Y
t itself. By an integration by part, we obtain that
(56)
1
t
∫
0
t
Y
s
d
s
‍
=
1
t
∫
0
G
(
t
)
Y
G
-
1
(
u
)
d
u
g
∘
G
-
1
(
u
)
=
1
t
g
(
t
)
∫
0
G
(
t
)
Y
G
-
1
(
u
)
d
u
+
1
t
‍
∫
0
G
(
t
)
d
u
g
′
∘
G
-
1
(
u
)
(
g
∘
G
-
1
(
u
)
)
3
‍
∫
0
u
Y
G
-
1
(
s
)
d
s
.
Corollary 20 implies that the first right-hand term converges in distribution to 0 because G
(
t
)
≤
t
g
(
t
). So it converges in probability to 0. It remains to prove the convergence of the second term. We recall that, up to a multiplicative positive constant, g
∘
G
-
1
(
u
)
=
log
⁡
⁡
(
2
+
u
). Moreover, we also know that (
1
/
u
)
∫
0
u
‍
Y
G
-
1
(
s
)
d
s is almost surely bounded. So, the second right-hand term is upper bounded (up to a multiplicative positive constant) by
(57)
1
t
∫
0
G
(
t
)
d
u
(
log
⁡
⁡
(
2
+
u
)
)
2
=
G
(
t
)
t
(
log
⁡
⁡
G
(
t
)
)
2
+
o
(
G
(
t
)
t
(
log
⁡
⁡
G
(
t
)
)
2
)
≤
g
(
t
)
log
⁡
⁡
G
(
t
)
1
log
⁡
⁡
G
(
t
)
and the result is as follows: μ
¯
t
=
U
t converges in distribution. And by Corollary 20, Y
t converges in distribution to Y
∞ which concentrates on the global minima of V. So, Y
t
=
U
t
-
W
t converges in probability to 0.
To conclude, ifV satisfies ∑
‍
1
≤
i
≤
n
a
i
m
i
≠
0, then μ
¯
t
=
∫
0
t
‍
Y
s
(
d
s
/
s
) does not converge and so X
t diverges.


## 4. Convergence in Distribution ofX to a Random Variable

In this Section, we will prove that ifg converges to 1 or 0 slowly enough, then the process X converges in distribution to an identified limit. We will first study the case g
≡
1 and prove rigorously the convergence of μ
¯
t. Then, we will consider the case g
(
t
)
→
0 and t
g
(
t
)
→
+
∞. The proof of the convergence of μ
¯
t will be exactly the same as in the case g
=
1 and so we will not reproduce it. Nevertheless, the convergence of Y will be interesting and Section 4.2 is essentially devoted to its proof.


### 4.1. Ifg Converges toward a Positive Constant

In this part, we suppose thatg converges toward a positive constant, so that its primitive G goes to the infinity. In that case, we will show that the asymptotic behaviour of Y is very close to the behaviour of ξ, solution to
(58)
d
ξ
t
=
d
B
t
-
g
(
t
)
∇
V
(
ξ
t
)
d
t
.
Without any loss of generality, we suppose that g
(
t
)
=
1 for t large enough. Actually, we will prove that Y converges toward a random variable of law Π
(
d
x
)
=
(
e
-
2
V
(
x
)
/
π
)
d
x. (Remark that the normalization constant π is well defined as V is strictly convex out of a compact set.) To this aim, we will use the exponential decrease to zero of the relative Kullback information between the law of Y
G
-
1
(
t
) and Π. Once this is done, we study the convergence of the mean (
1
/
t
)
∫
0
t
‍
Y
s
d
s. Indeed, we will prove that the latter integral converges if and only if ∫
x
d
Π
(
x
)
=
0.Theorem 24.
X
t
-
X
0 converges in distribution to Y
∞ if and only if ∫
x
e
-
2
V
(
x
)
d
x
=
0. In that case, Y
∞ has the distribution law e
-
2
V
/
π.The proof of this statement will be decomposed into several propositions and lemmas. We first present them all, postponing their proofs. Then, we deduce from them Theorem24. Finally, we prove these intermediate results. Let us state the first of the propositions mentioned, the one showing that the time-shifted process (
Y
G
-
1
(
t
)
)
t, and so (
Y
t
)
t converges in distribution.Proposition 25.
The process(
Y
t
)
t converges in distribution to a random variable Y
∞. The distribution law of Y
∞ is e
-
2
V
/
π.Next, we have to show that eitherμ
¯
t
=
∫
0
t
‍
(
Y
s
/
s
)
d
s converges almost surely toward x
=
X
0 for suitable functions V or diverges.Proposition 26.
μ
¯
t converges almost surely as t
→
∞ if and only if ∫
x
e
-
2
V
(
x
)
d
x
=
0.We are now ready to prove Theorem24.Proof of Theorem24.
Recall thatX
t
=
Y
t
+
μ
¯
t. Proposition 25 asserts that Y
t converges in distribution to Y
∞. Moreover, μ
¯
t converges almost surely to μ
¯
∞
=
x if and only if ∫
x
e
-
2
V
(
x
)
d
x
=
0 by Proposition 26. So, we use Slutsky’s theorem: μ
¯
t
-
μ
¯
∞ converges almost surely to 0, and Y
t converges in distribution to Y
∞, so Y
t
+
(
μ
¯
t
-
μ
¯
∞
) goes in distribution to Y
∞.Let us now prove Propositions25-26.Proof of Proposition25.
We will show that the process(
Y
t
)
t converges in distribution to Y
∞. Let p
t denote the law of Y
t. By Lemma 4, the probability measure Π
=
e
-
2
V
/
π (where π denotes the normalization constant of Π) satisfies a logarithmic Sobolev inequality LSI
(
C
LS
). By inequality (16), we know that
(59)
H
(
p
t
∣
Π
)
≤
C
LS
∫
‍
|
∇
(
p
t
Π
)
|
2
d
λ
.
As ∇
(
p
t
/
Π
)
=
π
p
t
(
e
V
/
2
)
(
∇
p
t
/
p
t
+
2
∇
V
), we deduce that
(60)
H
(
p
t
∣
Π
)
≤
C
LS
4
∫
p
t
|
∇
p
t
p
t
+
2
∇
V
|
2
d
λ
.
Moreover, by definition of the relative Kullback information, it is clear that (
d
/
d
t
)
H
(
p
t
∣
Π
)
=
∫
p
˙
t
log
⁡
⁡
(
p
t
/
Π
)
d
λ. The Kolmogorov-forward equation also reads
(61)
p
˙
t
=
1
2
Δ
p
t
+
(
∇
p
t
,
∇
V
)
=
∇
·
(
1
2
∇
p
t
+
p
t
∇
V
)
,
and putting this last estimate in the previous time-derivative equation of H, we have
(62)
d
d
t
H
(
p
t
∣
Π
)
=
∫
p
˙
t
log
⁡
⁡
(
p
t
Π
)
d
λ
=
∫
∇
·
(
1
2
∇
p
t
+
p
t
∇
V
)
log
⁡
⁡
(
p
t
Π
)
d
λ
=
-
∫
(
1
2
∇
p
t
+
p
t
∇
V
,
∇
p
t
p
t
+
2
∇
V
)
d
λ
=
-
1
2
∫
p
t
|
∇
p
t
p
t
+
2
∇
V
|
2
d
λ
≤
-
2
C
LS
H
(
p
t
∣
Π
)
.
So H
(
p
t
∣
Π
) converges to zero exponentially fast. This means that ∥
p
t
-
Π
∥
TV
2
≤
2
H
(
p
t
∣
Π
)
→
0; that is, Y
t converges in distribution toward a random variable Y
∞. The distribution law of Y
∞ is Π and the speed of convergence is exponential.Proof of Proposition26.
Lety
¯
t
:
=
(
1
/
t
)
∫
0
t
Y
s
d
s. We have to show that y
¯
t converges almost surely to y
¯
∞
=
∫
x
Π
(
d
x
). First, we decompose μ
¯
t in the following way:
(63)
μ
¯
t
=
x
+
∫
0
t
Y
s
d
s
s
=
x
+
1
t
∫
0
t
Y
s
d
s
+
∫
0
t
1
s
2
∫
0
s
Y
u
d
u
d
s
.
We then have
(64)
μ
¯
t
=
x
+
y
¯
t
+
∫
0
t
1
s
y
¯
s
d
s
.
Let us introduce the positive recurrent Kolmogorov process (
ξ
t
,
t
≥
0
), solution to d
ξ
t
=
d
B
t
-
∇
V
(
ξ
t
)
d
t. The invariant probability measure associated with ξ is precisely Π. As ξ is pointwise ergodic, we have for all h
∈
L
1
(
Π
)
(65)
lim
⁡
t
→
∞
1
t
∫
0
t
‍
h
(
ξ
s
)
d
s
=
∫
h
d
Π
a
.
s
.
with an exponential speed of convergence (see for instance [16]).
Let us now prove the almost sure convergence ofy
¯
t. We have
(66)
y
¯
e
t
+
s
-
y
¯
e
t
=
∫
t
t
+
s
(
Y
e
u
-
y
¯
e
u
)
d
u
=
∫
0
s
(
y
¯
∞
-
y
¯
e
t
+
u
)
d
u
+
∫
0
s
(
Y
e
t
+
u
-
y
¯
∞
)
d
u
.
We will now need the following technical result.
Lemma 27.For all
T
>
0
,
lim
⁡
t
→
∞
sup
⁡
0
≤
s
≤
T
|
∫
0
s
(
Y
e
t
+
u
-
y
¯
∞
)
d
u
|
=
0
almost surely.
Assuming the validity of this statement, the process(
y
¯
e
t
)
t is an asymptotic pseudotrajectory for the flow (
d
/
d
t
)
ϕ
t
(
x
)
=
y
¯
∞
-
ϕ
t
(
x
), ϕ
0
(
x
)
=
x. The flow induced by ϕ admits a unique limit point {
y
¯
∞
}, which is exponentially attracted. Thus, y
¯
t converges almost surely to y
¯
∞ (with an exponential speed of convergence).
Let us now estimate the distance betweenY
s
+
e
t and ξ
s
+
e
t, knowing that Y
e
t
=
ξ
e
t. As W is strictly convex and ∇
χ is C
χ-Lipschitz, we obtain the following inequality:
(67)
1
2
d
d
t
|
Y
s
+
e
t
-
ξ
s
+
e
t
|
2
=
-
(
∇
V
(
Y
s
+
e
t
)
-
∇
V
(
ξ
s
+
e
t
)
,
Y
s
+
e
t
-
ξ
s
+
e
t
)
-
1
s
+
e
t
(
Y
s
+
e
t
,
Y
s
+
e
t
-
ξ
s
+
e
t
)
≤
-
(
C
W
-
C
χ
)
|
Y
s
+
e
t
-
ξ
s
+
e
t
|
2
+
1
2
(
s
+
e
t
)
|
ξ
s
+
e
t
|
2
,
because -
2
(
y
,
y
-
z
)
≤
|
z
|
2. So we have the following bound on the square-distance:
(68)
|
Y
s
+
e
t
-
ξ
s
+
e
t
|
2
≤
e
-
2
(
C
W
-
C
χ
)
s
∫
0
s
e
(
C
W
-
C
χ
)
u
|
ξ
u
+
e
t
|
2
d
u
u
+
e
t
.
Once again, the ergodicity of ξ implies that (
1
/
s
)
∫
0
s
‍
|
ξ
u
+
e
t
|
2
d
u converges almost surely (as s
→
∞) to ∫
|
x
|
2
Π
(
d
x
). So there exists C
>
0 such that
(69)
∫
0
s
e
(
C
W
-
C
χ
)
u
|
ξ
u
+
e
t
|
2
d
u
u
+
e
t
≤
e
-
t
(
|
∫
0
s
e
(
C
W
-
C
χ
)
u
(
|
ξ
u
+
e
t
|
2
-
∫
|
x
|
2
Π
(
d
x
)
)
d
u
|
+
e
(
C
W
-
C
χ
)
s
C
W
-
C
χ
∫
|
x
|
2
Π
(
d
x
)
)
≤
C
e
-
t
e
(
C
W
-
C
χ
)
s
∫
‍
|
x
|
2
Π
(
d
x
)
.
And thus |
Y
s
+
e
t
-
ξ
s
+
e
t
|
2
≤
C
e
-
t almost surely. So
(70)
∫
0
e
t
(
e
s
-
1
)
Y
v
+
e
t
-
ξ
v
+
e
t
v
+
e
t
d
v
=
O
(
e
-
t
)
.
To prove Proposition 26, we use the decomposition (64). It is obvious from that decomposition that if y
¯
∞
=
∫
x
Π
(
d
x
)
≠
0, then μ
¯
t does not converge and in that case μ
¯
t
~
y
¯
∞
log
⁡
⁡
t. Suppose now that y
¯
∞
=
0. As
(71)
y
¯
s
=
1
s
∫
0
s
Y
u
d
u
=
1
s
∫
0
s
(
Y
u
-
ξ
u
)
d
u
+
1
s
∫
0
s
ξ
u
d
u
,
and by (65), there exists a positive constant a such that |
(
1
/
s
)
∫
0
s
‍
ξ
u
d
u
|
≤
e
-
a
s and we get that |
y
¯
s
|
=
O
(
s
-
a
) with a
>
0. So the integral ∫
0
t
‍
(
1
/
s
)
y
¯
s
d
s converges almost surely, implying the convergence of the empirical mean μ
¯
t.Proof of Lemma 27.
Lett
≥
0. We have
(72)
d
Y
s
+
e
t
=
d
B
s
+
e
t
-
(
∇
V
(
Y
s
+
e
t
)
+
Y
s
+
e
t
s
+
e
t
)
d
s
.
Let us prove that the drift term Y
s
+
e
t
/
(
s
+
e
t
) is negligible for t large enough. Let T
≥
0. For any 0
≤
s
≤
T, we have
(73)
∫
0
s
(
Y
e
t
+
u
-
y
¯
∞
)
d
u
=
∫
0
e
t
(
e
s
-
1
)
Y
v
+
e
t
-
ξ
v
+
e
t
v
+
e
t
d
v
+
∫
0
e
t
(
e
s
-
1
)
ξ
v
+
e
t
-
y
¯
∞
v
+
e
t
d
v
,
where ξ
e
t
=
Y
e
t. We emphasize that ξ and Y are driven by the same Brownian motion. We have already proved in (70) that the first right-hand term of (73) converges (exponentially fast) to 0. Let us now study the most right-hand side of (73). An integration by parts leads to
(74)
∫
0
e
t
(
e
s
-
1
)
ξ
v
+
e
t
-
y
¯
∞
v
+
e
t
d
v
=
e
s
-
1
e
s
(
1
e
t
(
e
s
-
1
)
∫
0
e
t
(
e
s
-
1
)
ξ
u
+
e
t
d
u
-
y
¯
∞
)
+
∫
0
e
t
(
e
s
-
1
)
v
(
v
+
e
t
)
2
(
1
v
∫
0
v
ξ
u
+
e
t
d
u
-
y
¯
∞
)
d
v
.
The ergodicity (65) of ξ implies directly that (
1
/
e
t
(
e
s
-
1
)
)
∫
0
e
t
(
e
s
-
1
)
ξ
u
+
e
t
d
u
-
y
¯
∞ converges almost surely to 0 (as t
→
∞), with an exponential speed of convergence. So there exist two positive constants a
,
C such that almost surely
(75)
|
∫
0
e
t
(
e
s
-
1
)
v
(
v
+
e
t
)
2
(
1
v
∫
0
v
‍
ξ
u
+
e
t
d
u
-
y
¯
∞
)
d
v
|
≤
e
-
2
t
∫
0
e
t
(
e
s
-
1
)
|
1
v
∫
0
v
ξ
u
+
e
t
d
u
-
y
¯
∞
|
d
v
≤
C
e
-
2
t
∫
0
e
t
(
e
s
-
1
)
e
-
a
v
d
v
⟶
t
→
∞
0
.
So lim
⁡
t
sup
⁡
0
≤
s
≤
T
|
∫
0
e
t
(
e
s
-
1
)
(
(
ξ
v
+
e
t
-
y
¯
∞
)
/
(
v
+
e
t
)
)
d
v
|
=
0 almost surely. Finally, putting all the pieces together we have shown that y
¯
e
t is an asymptotic pseudotrajectory for the flow generated by ϕ.


### 4.2. Ifg
(
t
) Goes to Zero and t
g
(
t
) Goes to c
∈
]
0
,
+
∞
]

The technique we adopt here is a change of scale added to a change of measure. This is useful as soon as we wish to study the asymptotic or ergodic behaviour of a nonhomogeneous process, as it usually permits to “reduce” to the homogeneous case.


#### 4.2.1. Ift
g
(
t
) Goes to a Positive Constant

In this part, we suppose without any loss of generality thatt
g
(
t
)
=
1. Indeed, Y is solution to the SDE:
(76)
d
Y
t
=
d
B
t
-
1
t
(
∇
V
(
Y
t
)
+
Y
t
)
d
t
.Theorem 28.
Suppose that there existsn
≥
1 such that V
(
x
)
/
|
x
|
2
n converges to a positive constant and g
(
t
)
=
1
/
t. Then, X
t converges in distribution to Y
∞
+
μ
¯
∞ if and only if ∫
‍
x
e
-
2
V
(
x
)
d
x
=
0 and n
>
3
/
2. In that case, Y
∞ has the distribution law e
-
2
V
(
x
)
d
x (up to a positive multiplicative constant).Proof.
The proof is similar to the one of Theorem24. If V is polynomial or V
(
x
)
=
c
|
x
|
2
n (for c
>
0), then letting Y
~
t
:
=
(
(
n
-
1
)
/
n
)
t
-
1
/
2
(
n
-
1
)
Y
t
n
/
(
n
-
1
), we find that Y
~ satisfies
(77)
d
Y
~
t
=
d
B
t
-
(
2
c
n
|
Y
~
t
|
2
n
-
2
Y
~
t
+
2
n
+
1
2
(
n
-
1
)
t
Y
~
t
)
d
t
.
Now, we can approximate the potential V by c
|
x
|
2
n (for a well-chosen n
≥
1) such that the studied process Y
~ has the same asymptotic behaviour as
(78)
d
ξ
t
=
d
B
t
-
(
∇
V
(
ξ
t
)
+
2
n
+
1
2
(
n
-
1
)
t
ξ
t
)
d
t
in the sense of asymptotic pseudotrajectory. Actually, one easily shows that if ∫
x
e
-
2
V
(
x
)
d
x
=
0, then for all T
>
0
(79)
sup
⁡
0
≤
s
≤
T
|
Y
~
s
+
e
t
-
ξ
s
+
e
t
|
2
=
O
(
e
-
t
)
.
This has been already shown for Lemma 27 and we do not reproduce the proof here. Finally, this proves that Y
~ has the same ergodic behaviour as ξ (see [17]). So as ξ is ergodic and almost surely (
1
/
t
)
∫
0
t
ξ
s
d
s converges, the limit points of (
1
/
t
)
∫
0
t
δ
Y
~
s
d
s are included into the set of the invariant measures of ξ, that is, e
-
2
V
(
x
)
d
x. So (
1
/
t
)
∫
0
t
Y
~
s
d
s goes to zero if and only if ∫
x
e
-
2
V
(
x
)
d
x
=
0. By (79), the asymptotic pseudotrajectory has a polynomial speed. Moreover, ξ converges to its invariant probability measure with an exponential speed. Thus, (
1
/
t
)
∫
0
t
‍
Y
~
s
d
s converges almost surely to zero with a polynomial speed of convergence (of the order of 1
/
t) if ∫
x
e
-
2
V
(
x
)
d
x
=
0 (and diverges otherwise). Now, remembering that Y
~
s
=
s
-
1
/
2
(
n
-
1
)
Y
s
n
/
(
n
-
1
), we conclude that
(80)
1
t
∫
0
t
Y
~
s
d
s
=
1
t
∫
0
t
n
/
(
n
-
1
)
Y
u
d
u
u
3
/
2
n
⟶
0
a
.
s
.
Indeed, we have
(81)
1
t
∫
0
t
n
/
(
n
-
1
)
Y
u
d
u
u
3
/
2
n
=
1
t
1
+
3
/
2
(
n
-
1
)
∫
0
t
n
/
(
n
-
1
)
Y
u
d
u
-
1
t
∫
0
t
n
/
(
n
-
1
)
u
-
1
-
3
/
2
n
∫
0
u
Y
s
d
s
d
u
=
O
(
t
-
1
)
a
.
s
.
And thus, (
1
/
T
)
∫
0
T
Y
u
d
u converges almost surely to 0 (with T
=
t
n
/
(
n
-
1
)) if and only if n
>
3
/
2. We then refer to Section 4.1 to obtain the convergence of the process X.We also remark that this result is coherent with the basic Ornstein-Uhlenbeck case.


#### 4.2.2. Ift
g
(
t
) Goes to the Infinity

This study will be divided into two different cases. First, we suppose that there exists0
<
α
<
1 such that t
α
g
(
t
) goes to a positive constant. Whereas in the second case, t
α
g
(
t
) goes to the infinity for any 0
<
α
<
1 (this is for instance satisfied by g
(
t
)
=
1
/
log
⁡
⁡
t). The first part of the study is identical for the two cases and we will only divide the end of the study.Theorem 29.
Suppose that there existsn
≥
1 such that V
(
x
)
/
|
x
|
2
n converges to a positive constant.(1)
If there exists0
<
α
<
1 such that g
(
t
)
=
t
α, then X
t converges in distribution to Y
∞
+
μ
¯
∞ if and only if ∫
x
e
-
2
V
(
x
)
d
x
=
0 and n
>
4
α.
(2)
Ift
α
g
(
t
)
→
+
∞ for all 0
<
α
<
1, then X
t converges in distribution to Y
∞
+
μ
¯
∞ if and only if ∫
x
e
-
2
V
(
x
)
d
x
=
0.Let us sketch the proof of Theorem29 that is postponed to the end of the paragraph. As in the preceding paragraph, we first suppose that V
(
x
)
=
c
|
x
|
2
n. Define f as the positive increasing solution to g
∘
f
(
t
)
=
(
f
′
(
t
)
)
-
n. Consider the time and scale-changed process Y
~ defined by Y
~
t
:
=
Y
f
(
t
)
/
f
′
(
t
). Applying Itô's formula to Y
~, we thus find that Y
~ satisfies the SDE:
(82)
d
Y
~
t
=
d
B
t
-
(
2
n
c
Y
~
t
|
Y
~
t
|
2
n
-
2
+
(
f
′
(
t
)
f
(
t
)
+
f
′′
(
t
)
2
f
′
(
t
)
)
Y
~
t
)
d
t
.
Now, we approximate the potential V by |
x
|
2
n for a well-chosen n
≥
1. So the studied process Y
~ has the same asymptotic behaviour as
(83)
d
ξ
t
=
d
B
t
-
(
∇
V
(
ξ
t
)
+
ξ
t
β
(
t
)
)
d
t
,
where β
(
t
) is defined by 1
/
β
(
t
)
=
f
′
(
t
)
/
f
(
t
)
+
f
′′
(
t
)
/
2
f
′
(
t
) (this last quantity goes to 0 as t tends to +
∞).Define also the processY
^ as the solution to the SDE
(84)
d
Y
^
t
=
d
B
t
-
∇
V
(
Y
^
t
)
d
t
.Lemma 30.
The processξ (and also Y
~) is an asymptotic pseudotrajectory for the process Y
^: for all t
,
T
>
0, we have sup
⁡
0
≤
s
≤
T
|
Y
^
s
+
t
-
ξ
s
+
t
|
=
O
(
β
(
t
)
-
1
/
2
) almost surely.Proof.
Lett
>
0 and 0
≤
s
≤
T. Itô's formula implies that
(85)
1
2
d
d
t
|
Y
^
s
+
t
-
ξ
s
+
t
|
2
=
-
(
∇
V
(
Y
^
s
+
t
)
-
∇
V
(
ξ
s
+
t
)
,
Y
^
s
+
t
-
ξ
s
+
t
)
+
1
β
(
s
+
t
)
(
ξ
s
+
t
,
Y
^
s
+
t
-
ξ
s
+
t
)
≤
-
(
C
W
-
C
χ
)
|
Y
^
s
+
t
-
ξ
s
+
t
|
2
+
1
2
β
(
s
+
t
)
|
Y
^
s
+
t
|
2
.
So we find that
(86)
|
Y
^
s
+
t
-
ξ
s
+
t
|
2
≤
e
-
2
(
C
W
-
C
χ
)
s
∫
0
s
e
(
C
W
-
C
χ
)
u
|
Y
^
u
+
t
|
2
d
u
β
(
u
+
t
)
.
Let us note π
=
∫
‍
e
-
2
V
(
x
)
d
x the normalisation constant. The ergodicity of Y
^ implies the existence of C
>
0 such that
(87)
∫
0
s
e
(
C
W
-
C
χ
)
u
|
Y
^
u
+
t
|
2
d
u
β
(
u
+
t
)
≤
1
β
(
t
)
{
|
∫
0
s
e
(
C
W
-
C
χ
)
u
(
|
Y
^
u
+
t
|
2
-
∫
|
x
|
2
e
-
2
V
(
x
)
π
d
x
‍
)
d
u
‍
|
+
e
(
C
W
-
C
χ
)
s
C
W
-
C
χ
∫
|
x
|
2
e
-
2
V
(
x
)
π
d
x
‍
}
≤
C
β
(
t
)
e
(
C
W
-
C
χ
)
s
∫
|
x
|
2
e
-
2
V
(
x
)
d
x
‍
.
This leads to the result. And also there exists a constant M
>
0 such that almost surely
(88)
sup
⁡
0
≤
s
≤
T
|
Y
^
s
+
t
-
ξ
s
+
t
|
2
≤
M
β
(
t
)
.Proof of Theorem29.
Lemma30 proves that (
1
/
t
)
∫
0
t
‍
Y
~
u
d
u converges almost surely to 0 if and only ∫
‍
x
e
-
2
V
(
x
)
d
x
=
0. Moreover, we see by a similar equation (88) that Y
~ is an asymptotic pseudotrajectory for Y
^ with the speed of convergence 1
/
β
(
t
). As Y
^ converges to its invariant probability measure e
-
2
V
(
x
)
d
x with an exponential speed of convergence, we find that (
1
/
t
)
∫
0
t
‍
Y
~
s
d
s converges almost surely to 0 if and only if ∫
‍
x
e
-
2
V
(
x
)
d
x
=
0 and in that case, we have the following result, depending on the function g.(1)
First, suppose thatg
(
t
)
=
t
-
α for a given 0
<
α
<
1. We thus have f
(
t
)
=
t
n
/
(
n
-
α
) and (
1
/
t
)
∫
0
t
‍
Y
s
n
/
(
n
-
α
)
(
d
s
/
s
α
/
2
(
n
-
α
)
)
=
(
1
/
t
)
∫
0
t
n
/
(
n
-
α
)
‍
Y
u
(
d
u
/
u
3
α
/
2
n
) converge to 0 if and only if ∫
‍
x
e
-
2
V
(
x
)
d
x
=
0. Then, almost surely |
(
1
/
T
)
∫
0
T
‍
Y
u
d
u
| behaves asymptotically as T
(
2
α
/
n
)
-
(
1
/
2
), where T
=
t
n
/
(
n
-
α
) and thus it converges to zero if and only if n
>
4
α.
(2)
Suppose now thatt
α
g
(
t
)
→
∞ for all 0
<
α
<
1. This implies that g
(
t
)
≥
t
-
α for any 0
<
α
<
1 and so f
(
t
)
≤
t
n
/
(
n
-
α
) and 0
≤
-
g
′
(
t
)
/
g
(
t
)
≤
α
/
t. So
(89)
1
t
∫
0
t
‍
Y
~
s
d
s
=
1
t
∫
f
(
0
)
f
(
t
)
‍
Y
u
d
u
(
f
′
∘
f
-
1
(
u
)
)
3
/
2
and forT
=
f
(
t
), the mean |
(
1
/
T
)
∫
0
T
‍
Y
u
d
u
| is almost surely upper bounded by f
(
t
)
/
t
(
f
′
(
t
)
)
3
/
2
β
(
t
). As
(90)
1
β
(
t
)
=
f
′
(
t
)
f
(
t
)
+
f
′′
(
t
)
2
f
(
t
)
≤
(
g
∘
f
(
t
)
)
-
1
/
n
f
+
α
n
f
(
t
)
(
g
∘
f
(
t
)
)
2
/
n
≤
1
f
(
t
)
(
g
∘
f
(
t
)
)
1
/
n
,
we find that(91)
f
(
t
)
t
(
f
′
(
t
)
)
3
/
2
β
(
t
)
≤
f
(
t
)
(
g
∘
f
(
t
)
)
1
/
2
n
t
≤
(
g
∘
f
(
t
)
)
1
/
2
n
t
(
n
/
2
(
n
-
α
)
)
-
1
.This last term goes to 0 if n
>
2
α. As α is arbitrary chosen between 0 and 1, we conclude that |
(
1
/
T
)
∫
0
T
‍
Y
u
d
u
| converges almost surely to 0 for any n
≥
1.


## 4.1. Ifg Converges toward a Positive Constant

In this part, we suppose thatg converges toward a positive constant, so that its primitive G goes to the infinity. In that case, we will show that the asymptotic behaviour of Y is very close to the behaviour of ξ, solution to
(58)
d
ξ
t
=
d
B
t
-
g
(
t
)
∇
V
(
ξ
t
)
d
t
.
Without any loss of generality, we suppose that g
(
t
)
=
1 for t large enough. Actually, we will prove that Y converges toward a random variable of law Π
(
d
x
)
=
(
e
-
2
V
(
x
)
/
π
)
d
x. (Remark that the normalization constant π is well defined as V is strictly convex out of a compact set.) To this aim, we will use the exponential decrease to zero of the relative Kullback information between the law of Y
G
-
1
(
t
) and Π. Once this is done, we study the convergence of the mean (
1
/
t
)
∫
0
t
‍
Y
s
d
s. Indeed, we will prove that the latter integral converges if and only if ∫
x
d
Π
(
x
)
=
0.Theorem 24.
X
t
-
X
0 converges in distribution to Y
∞ if and only if ∫
x
e
-
2
V
(
x
)
d
x
=
0. In that case, Y
∞ has the distribution law e
-
2
V
/
π.The proof of this statement will be decomposed into several propositions and lemmas. We first present them all, postponing their proofs. Then, we deduce from them Theorem24. Finally, we prove these intermediate results. Let us state the first of the propositions mentioned, the one showing that the time-shifted process (
Y
G
-
1
(
t
)
)
t, and so (
Y
t
)
t converges in distribution.Proposition 25.
The process(
Y
t
)
t converges in distribution to a random variable Y
∞. The distribution law of Y
∞ is e
-
2
V
/
π.Next, we have to show that eitherμ
¯
t
=
∫
0
t
‍
(
Y
s
/
s
)
d
s converges almost surely toward x
=
X
0 for suitable functions V or diverges.Proposition 26.
μ
¯
t converges almost surely as t
→
∞ if and only if ∫
x
e
-
2
V
(
x
)
d
x
=
0.We are now ready to prove Theorem24.Proof of Theorem24.
Recall thatX
t
=
Y
t
+
μ
¯
t. Proposition 25 asserts that Y
t converges in distribution to Y
∞. Moreover, μ
¯
t converges almost surely to μ
¯
∞
=
x if and only if ∫
x
e
-
2
V
(
x
)
d
x
=
0 by Proposition 26. So, we use Slutsky’s theorem: μ
¯
t
-
μ
¯
∞ converges almost surely to 0, and Y
t converges in distribution to Y
∞, so Y
t
+
(
μ
¯
t
-
μ
¯
∞
) goes in distribution to Y
∞.Let us now prove Propositions25-26.Proof of Proposition25.
We will show that the process(
Y
t
)
t converges in distribution to Y
∞. Let p
t denote the law of Y
t. By Lemma 4, the probability measure Π
=
e
-
2
V
/
π (where π denotes the normalization constant of Π) satisfies a logarithmic Sobolev inequality LSI
(
C
LS
). By inequality (16), we know that
(59)
H
(
p
t
∣
Π
)
≤
C
LS
∫
‍
|
∇
(
p
t
Π
)
|
2
d
λ
.
As ∇
(
p
t
/
Π
)
=
π
p
t
(
e
V
/
2
)
(
∇
p
t
/
p
t
+
2
∇
V
), we deduce that
(60)
H
(
p
t
∣
Π
)
≤
C
LS
4
∫
p
t
|
∇
p
t
p
t
+
2
∇
V
|
2
d
λ
.
Moreover, by definition of the relative Kullback information, it is clear that (
d
/
d
t
)
H
(
p
t
∣
Π
)
=
∫
p
˙
t
log
⁡
⁡
(
p
t
/
Π
)
d
λ. The Kolmogorov-forward equation also reads
(61)
p
˙
t
=
1
2
Δ
p
t
+
(
∇
p
t
,
∇
V
)
=
∇
·
(
1
2
∇
p
t
+
p
t
∇
V
)
,
and putting this last estimate in the previous time-derivative equation of H, we have
(62)
d
d
t
H
(
p
t
∣
Π
)
=
∫
p
˙
t
log
⁡
⁡
(
p
t
Π
)
d
λ
=
∫
∇
·
(
1
2
∇
p
t
+
p
t
∇
V
)
log
⁡
⁡
(
p
t
Π
)
d
λ
=
-
∫
(
1
2
∇
p
t
+
p
t
∇
V
,
∇
p
t
p
t
+
2
∇
V
)
d
λ
=
-
1
2
∫
p
t
|
∇
p
t
p
t
+
2
∇
V
|
2
d
λ
≤
-
2
C
LS
H
(
p
t
∣
Π
)
.
So H
(
p
t
∣
Π
) converges to zero exponentially fast. This means that ∥
p
t
-
Π
∥
TV
2
≤
2
H
(
p
t
∣
Π
)
→
0; that is, Y
t converges in distribution toward a random variable Y
∞. The distribution law of Y
∞ is Π and the speed of convergence is exponential.Proof of Proposition26.
Lety
¯
t
:
=
(
1
/
t
)
∫
0
t
Y
s
d
s. We have to show that y
¯
t converges almost surely to y
¯
∞
=
∫
x
Π
(
d
x
). First, we decompose μ
¯
t in the following way:
(63)
μ
¯
t
=
x
+
∫
0
t
Y
s
d
s
s
=
x
+
1
t
∫
0
t
Y
s
d
s
+
∫
0
t
1
s
2
∫
0
s
Y
u
d
u
d
s
.
We then have
(64)
μ
¯
t
=
x
+
y
¯
t
+
∫
0
t
1
s
y
¯
s
d
s
.
Let us introduce the positive recurrent Kolmogorov process (
ξ
t
,
t
≥
0
), solution to d
ξ
t
=
d
B
t
-
∇
V
(
ξ
t
)
d
t. The invariant probability measure associated with ξ is precisely Π. As ξ is pointwise ergodic, we have for all h
∈
L
1
(
Π
)
(65)
lim
⁡
t
→
∞
1
t
∫
0
t
‍
h
(
ξ
s
)
d
s
=
∫
h
d
Π
a
.
s
.
with an exponential speed of convergence (see for instance [16]).
Let us now prove the almost sure convergence ofy
¯
t. We have
(66)
y
¯
e
t
+
s
-
y
¯
e
t
=
∫
t
t
+
s
(
Y
e
u
-
y
¯
e
u
)
d
u
=
∫
0
s
(
y
¯
∞
-
y
¯
e
t
+
u
)
d
u
+
∫
0
s
(
Y
e
t
+
u
-
y
¯
∞
)
d
u
.
We will now need the following technical result.
Lemma 27.For all
T
>
0
,
lim
⁡
t
→
∞
sup
⁡
0
≤
s
≤
T
|
∫
0
s
(
Y
e
t
+
u
-
y
¯
∞
)
d
u
|
=
0
almost surely.
Assuming the validity of this statement, the process(
y
¯
e
t
)
t is an asymptotic pseudotrajectory for the flow (
d
/
d
t
)
ϕ
t
(
x
)
=
y
¯
∞
-
ϕ
t
(
x
), ϕ
0
(
x
)
=
x. The flow induced by ϕ admits a unique limit point {
y
¯
∞
}, which is exponentially attracted. Thus, y
¯
t converges almost surely to y
¯
∞ (with an exponential speed of convergence).
Let us now estimate the distance betweenY
s
+
e
t and ξ
s
+
e
t, knowing that Y
e
t
=
ξ
e
t. As W is strictly convex and ∇
χ is C
χ-Lipschitz, we obtain the following inequality:
(67)
1
2
d
d
t
|
Y
s
+
e
t
-
ξ
s
+
e
t
|
2
=
-
(
∇
V
(
Y
s
+
e
t
)
-
∇
V
(
ξ
s
+
e
t
)
,
Y
s
+
e
t
-
ξ
s
+
e
t
)
-
1
s
+
e
t
(
Y
s
+
e
t
,
Y
s
+
e
t
-
ξ
s
+
e
t
)
≤
-
(
C
W
-
C
χ
)
|
Y
s
+
e
t
-
ξ
s
+
e
t
|
2
+
1
2
(
s
+
e
t
)
|
ξ
s
+
e
t
|
2
,
because -
2
(
y
,
y
-
z
)
≤
|
z
|
2. So we have the following bound on the square-distance:
(68)
|
Y
s
+
e
t
-
ξ
s
+
e
t
|
2
≤
e
-
2
(
C
W
-
C
χ
)
s
∫
0
s
e
(
C
W
-
C
χ
)
u
|
ξ
u
+
e
t
|
2
d
u
u
+
e
t
.
Once again, the ergodicity of ξ implies that (
1
/
s
)
∫
0
s
‍
|
ξ
u
+
e
t
|
2
d
u converges almost surely (as s
→
∞) to ∫
|
x
|
2
Π
(
d
x
). So there exists C
>
0 such that
(69)
∫
0
s
e
(
C
W
-
C
χ
)
u
|
ξ
u
+
e
t
|
2
d
u
u
+
e
t
≤
e
-
t
(
|
∫
0
s
e
(
C
W
-
C
χ
)
u
(
|
ξ
u
+
e
t
|
2
-
∫
|
x
|
2
Π
(
d
x
)
)
d
u
|
+
e
(
C
W
-
C
χ
)
s
C
W
-
C
χ
∫
|
x
|
2
Π
(
d
x
)
)
≤
C
e
-
t
e
(
C
W
-
C
χ
)
s
∫
‍
|
x
|
2
Π
(
d
x
)
.
And thus |
Y
s
+
e
t
-
ξ
s
+
e
t
|
2
≤
C
e
-
t almost surely. So
(70)
∫
0
e
t
(
e
s
-
1
)
Y
v
+
e
t
-
ξ
v
+
e
t
v
+
e
t
d
v
=
O
(
e
-
t
)
.
To prove Proposition 26, we use the decomposition (64). It is obvious from that decomposition that if y
¯
∞
=
∫
x
Π
(
d
x
)
≠
0, then μ
¯
t does not converge and in that case μ
¯
t
~
y
¯
∞
log
⁡
⁡
t. Suppose now that y
¯
∞
=
0. As
(71)
y
¯
s
=
1
s
∫
0
s
Y
u
d
u
=
1
s
∫
0
s
(
Y
u
-
ξ
u
)
d
u
+
1
s
∫
0
s
ξ
u
d
u
,
and by (65), there exists a positive constant a such that |
(
1
/
s
)
∫
0
s
‍
ξ
u
d
u
|
≤
e
-
a
s and we get that |
y
¯
s
|
=
O
(
s
-
a
) with a
>
0. So the integral ∫
0
t
‍
(
1
/
s
)
y
¯
s
d
s converges almost surely, implying the convergence of the empirical mean μ
¯
t.Proof of Lemma 27.
Lett
≥
0. We have
(72)
d
Y
s
+
e
t
=
d
B
s
+
e
t
-
(
∇
V
(
Y
s
+
e
t
)
+
Y
s
+
e
t
s
+
e
t
)
d
s
.
Let us prove that the drift term Y
s
+
e
t
/
(
s
+
e
t
) is negligible for t large enough. Let T
≥
0. For any 0
≤
s
≤
T, we have
(73)
∫
0
s
(
Y
e
t
+
u
-
y
¯
∞
)
d
u
=
∫
0
e
t
(
e
s
-
1
)
Y
v
+
e
t
-
ξ
v
+
e
t
v
+
e
t
d
v
+
∫
0
e
t
(
e
s
-
1
)
ξ
v
+
e
t
-
y
¯
∞
v
+
e
t
d
v
,
where ξ
e
t
=
Y
e
t. We emphasize that ξ and Y are driven by the same Brownian motion. We have already proved in (70) that the first right-hand term of (73) converges (exponentially fast) to 0. Let us now study the most right-hand side of (73). An integration by parts leads to
(74)
∫
0
e
t
(
e
s
-
1
)
ξ
v
+
e
t
-
y
¯
∞
v
+
e
t
d
v
=
e
s
-
1
e
s
(
1
e
t
(
e
s
-
1
)
∫
0
e
t
(
e
s
-
1
)
ξ
u
+
e
t
d
u
-
y
¯
∞
)
+
∫
0
e
t
(
e
s
-
1
)
v
(
v
+
e
t
)
2
(
1
v
∫
0
v
ξ
u
+
e
t
d
u
-
y
¯
∞
)
d
v
.
The ergodicity (65) of ξ implies directly that (
1
/
e
t
(
e
s
-
1
)
)
∫
0
e
t
(
e
s
-
1
)
ξ
u
+
e
t
d
u
-
y
¯
∞ converges almost surely to 0 (as t
→
∞), with an exponential speed of convergence. So there exist two positive constants a
,
C such that almost surely
(75)
|
∫
0
e
t
(
e
s
-
1
)
v
(
v
+
e
t
)
2
(
1
v
∫
0
v
‍
ξ
u
+
e
t
d
u
-
y
¯
∞
)
d
v
|
≤
e
-
2
t
∫
0
e
t
(
e
s
-
1
)
|
1
v
∫
0
v
ξ
u
+
e
t
d
u
-
y
¯
∞
|
d
v
≤
C
e
-
2
t
∫
0
e
t
(
e
s
-
1
)
e
-
a
v
d
v
⟶
t
→
∞
0
.
So lim
⁡
t
sup
⁡
0
≤
s
≤
T
|
∫
0
e
t
(
e
s
-
1
)
(
(
ξ
v
+
e
t
-
y
¯
∞
)
/
(
v
+
e
t
)
)
d
v
|
=
0 almost surely. Finally, putting all the pieces together we have shown that y
¯
e
t is an asymptotic pseudotrajectory for the flow generated by ϕ.


## 4.2. Ifg
(
t
) Goes to Zero and t
g
(
t
) Goes to c
∈
]
0
,
+
∞
]

The technique we adopt here is a change of scale added to a change of measure. This is useful as soon as we wish to study the asymptotic or ergodic behaviour of a nonhomogeneous process, as it usually permits to “reduce” to the homogeneous case.


### 4.2.1. Ift
g
(
t
) Goes to a Positive Constant

In this part, we suppose without any loss of generality thatt
g
(
t
)
=
1. Indeed, Y is solution to the SDE:
(76)
d
Y
t
=
d
B
t
-
1
t
(
∇
V
(
Y
t
)
+
Y
t
)
d
t
.Theorem 28.
Suppose that there existsn
≥
1 such that V
(
x
)
/
|
x
|
2
n converges to a positive constant and g
(
t
)
=
1
/
t. Then, X
t converges in distribution to Y
∞
+
μ
¯
∞ if and only if ∫
‍
x
e
-
2
V
(
x
)
d
x
=
0 and n
>
3
/
2. In that case, Y
∞ has the distribution law e
-
2
V
(
x
)
d
x (up to a positive multiplicative constant).Proof.
The proof is similar to the one of Theorem24. If V is polynomial or V
(
x
)
=
c
|
x
|
2
n (for c
>
0), then letting Y
~
t
:
=
(
(
n
-
1
)
/
n
)
t
-
1
/
2
(
n
-
1
)
Y
t
n
/
(
n
-
1
), we find that Y
~ satisfies
(77)
d
Y
~
t
=
d
B
t
-
(
2
c
n
|
Y
~
t
|
2
n
-
2
Y
~
t
+
2
n
+
1
2
(
n
-
1
)
t
Y
~
t
)
d
t
.
Now, we can approximate the potential V by c
|
x
|
2
n (for a well-chosen n
≥
1) such that the studied process Y
~ has the same asymptotic behaviour as
(78)
d
ξ
t
=
d
B
t
-
(
∇
V
(
ξ
t
)
+
2
n
+
1
2
(
n
-
1
)
t
ξ
t
)
d
t
in the sense of asymptotic pseudotrajectory. Actually, one easily shows that if ∫
x
e
-
2
V
(
x
)
d
x
=
0, then for all T
>
0
(79)
sup
⁡
0
≤
s
≤
T
|
Y
~
s
+
e
t
-
ξ
s
+
e
t
|
2
=
O
(
e
-
t
)
.
This has been already shown for Lemma 27 and we do not reproduce the proof here. Finally, this proves that Y
~ has the same ergodic behaviour as ξ (see [17]). So as ξ is ergodic and almost surely (
1
/
t
)
∫
0
t
ξ
s
d
s converges, the limit points of (
1
/
t
)
∫
0
t
δ
Y
~
s
d
s are included into the set of the invariant measures of ξ, that is, e
-
2
V
(
x
)
d
x. So (
1
/
t
)
∫
0
t
Y
~
s
d
s goes to zero if and only if ∫
x
e
-
2
V
(
x
)
d
x
=
0. By (79), the asymptotic pseudotrajectory has a polynomial speed. Moreover, ξ converges to its invariant probability measure with an exponential speed. Thus, (
1
/
t
)
∫
0
t
‍
Y
~
s
d
s converges almost surely to zero with a polynomial speed of convergence (of the order of 1
/
t) if ∫
x
e
-
2
V
(
x
)
d
x
=
0 (and diverges otherwise). Now, remembering that Y
~
s
=
s
-
1
/
2
(
n
-
1
)
Y
s
n
/
(
n
-
1
), we conclude that
(80)
1
t
∫
0
t
Y
~
s
d
s
=
1
t
∫
0
t
n
/
(
n
-
1
)
Y
u
d
u
u
3
/
2
n
⟶
0
a
.
s
.
Indeed, we have
(81)
1
t
∫
0
t
n
/
(
n
-
1
)
Y
u
d
u
u
3
/
2
n
=
1
t
1
+
3
/
2
(
n
-
1
)
∫
0
t
n
/
(
n
-
1
)
Y
u
d
u
-
1
t
∫
0
t
n
/
(
n
-
1
)
u
-
1
-
3
/
2
n
∫
0
u
Y
s
d
s
d
u
=
O
(
t
-
1
)
a
.
s
.
And thus, (
1
/
T
)
∫
0
T
Y
u
d
u converges almost surely to 0 (with T
=
t
n
/
(
n
-
1
)) if and only if n
>
3
/
2. We then refer to Section 4.1 to obtain the convergence of the process X.We also remark that this result is coherent with the basic Ornstein-Uhlenbeck case.


### 4.2.2. Ift
g
(
t
) Goes to the Infinity

This study will be divided into two different cases. First, we suppose that there exists0
<
α
<
1 such that t
α
g
(
t
) goes to a positive constant. Whereas in the second case, t
α
g
(
t
) goes to the infinity for any 0
<
α
<
1 (this is for instance satisfied by g
(
t
)
=
1
/
log
⁡
⁡
t). The first part of the study is identical for the two cases and we will only divide the end of the study.Theorem 29.
Suppose that there existsn
≥
1 such that V
(
x
)
/
|
x
|
2
n converges to a positive constant.(1)
If there exists0
<
α
<
1 such that g
(
t
)
=
t
α, then X
t converges in distribution to Y
∞
+
μ
¯
∞ if and only if ∫
x
e
-
2
V
(
x
)
d
x
=
0 and n
>
4
α.
(2)
Ift
α
g
(
t
)
→
+
∞ for all 0
<
α
<
1, then X
t converges in distribution to Y
∞
+
μ
¯
∞ if and only if ∫
x
e
-
2
V
(
x
)
d
x
=
0.Let us sketch the proof of Theorem29 that is postponed to the end of the paragraph. As in the preceding paragraph, we first suppose that V
(
x
)
=
c
|
x
|
2
n. Define f as the positive increasing solution to g
∘
f
(
t
)
=
(
f
′
(
t
)
)
-
n. Consider the time and scale-changed process Y
~ defined by Y
~
t
:
=
Y
f
(
t
)
/
f
′
(
t
). Applying Itô's formula to Y
~, we thus find that Y
~ satisfies the SDE:
(82)
d
Y
~
t
=
d
B
t
-
(
2
n
c
Y
~
t
|
Y
~
t
|
2
n
-
2
+
(
f
′
(
t
)
f
(
t
)
+
f
′′
(
t
)
2
f
′
(
t
)
)
Y
~
t
)
d
t
.
Now, we approximate the potential V by |
x
|
2
n for a well-chosen n
≥
1. So the studied process Y
~ has the same asymptotic behaviour as
(83)
d
ξ
t
=
d
B
t
-
(
∇
V
(
ξ
t
)
+
ξ
t
β
(
t
)
)
d
t
,
where β
(
t
) is defined by 1
/
β
(
t
)
=
f
′
(
t
)
/
f
(
t
)
+
f
′′
(
t
)
/
2
f
′
(
t
) (this last quantity goes to 0 as t tends to +
∞).Define also the processY
^ as the solution to the SDE
(84)
d
Y
^
t
=
d
B
t
-
∇
V
(
Y
^
t
)
d
t
.Lemma 30.
The processξ (and also Y
~) is an asymptotic pseudotrajectory for the process Y
^: for all t
,
T
>
0, we have sup
⁡
0
≤
s
≤
T
|
Y
^
s
+
t
-
ξ
s
+
t
|
=
O
(
β
(
t
)
-
1
/
2
) almost surely.Proof.
Lett
>
0 and 0
≤
s
≤
T. Itô's formula implies that
(85)
1
2
d
d
t
|
Y
^
s
+
t
-
ξ
s
+
t
|
2
=
-
(
∇
V
(
Y
^
s
+
t
)
-
∇
V
(
ξ
s
+
t
)
,
Y
^
s
+
t
-
ξ
s
+
t
)
+
1
β
(
s
+
t
)
(
ξ
s
+
t
,
Y
^
s
+
t
-
ξ
s
+
t
)
≤
-
(
C
W
-
C
χ
)
|
Y
^
s
+
t
-
ξ
s
+
t
|
2
+
1
2
β
(
s
+
t
)
|
Y
^
s
+
t
|
2
.
So we find that
(86)
|
Y
^
s
+
t
-
ξ
s
+
t
|
2
≤
e
-
2
(
C
W
-
C
χ
)
s
∫
0
s
e
(
C
W
-
C
χ
)
u
|
Y
^
u
+
t
|
2
d
u
β
(
u
+
t
)
.
Let us note π
=
∫
‍
e
-
2
V
(
x
)
d
x the normalisation constant. The ergodicity of Y
^ implies the existence of C
>
0 such that
(87)
∫
0
s
e
(
C
W
-
C
χ
)
u
|
Y
^
u
+
t
|
2
d
u
β
(
u
+
t
)
≤
1
β
(
t
)
{
|
∫
0
s
e
(
C
W
-
C
χ
)
u
(
|
Y
^
u
+
t
|
2
-
∫
|
x
|
2
e
-
2
V
(
x
)
π
d
x
‍
)
d
u
‍
|
+
e
(
C
W
-
C
χ
)
s
C
W
-
C
χ
∫
|
x
|
2
e
-
2
V
(
x
)
π
d
x
‍
}
≤
C
β
(
t
)
e
(
C
W
-
C
χ
)
s
∫
|
x
|
2
e
-
2
V
(
x
)
d
x
‍
.
This leads to the result. And also there exists a constant M
>
0 such that almost surely
(88)
sup
⁡
0
≤
s
≤
T
|
Y
^
s
+
t
-
ξ
s
+
t
|
2
≤
M
β
(
t
)
.Proof of Theorem29.
Lemma30 proves that (
1
/
t
)
∫
0
t
‍
Y
~
u
d
u converges almost surely to 0 if and only ∫
‍
x
e
-
2
V
(
x
)
d
x
=
0. Moreover, we see by a similar equation (88) that Y
~ is an asymptotic pseudotrajectory for Y
^ with the speed of convergence 1
/
β
(
t
). As Y
^ converges to its invariant probability measure e
-
2
V
(
x
)
d
x with an exponential speed of convergence, we find that (
1
/
t
)
∫
0
t
‍
Y
~
s
d
s converges almost surely to 0 if and only if ∫
‍
x
e
-
2
V
(
x
)
d
x
=
0 and in that case, we have the following result, depending on the function g.(1)
First, suppose thatg
(
t
)
=
t
-
α for a given 0
<
α
<
1. We thus have f
(
t
)
=
t
n
/
(
n
-
α
) and (
1
/
t
)
∫
0
t
‍
Y
s
n
/
(
n
-
α
)
(
d
s
/
s
α
/
2
(
n
-
α
)
)
=
(
1
/
t
)
∫
0
t
n
/
(
n
-
α
)
‍
Y
u
(
d
u
/
u
3
α
/
2
n
) converge to 0 if and only if ∫
‍
x
e
-
2
V
(
x
)
d
x
=
0. Then, almost surely |
(
1
/
T
)
∫
0
T
‍
Y
u
d
u
| behaves asymptotically as T
(
2
α
/
n
)
-
(
1
/
2
), where T
=
t
n
/
(
n
-
α
) and thus it converges to zero if and only if n
>
4
α.
(2)
Suppose now thatt
α
g
(
t
)
→
∞ for all 0
<
α
<
1. This implies that g
(
t
)
≥
t
-
α for any 0
<
α
<
1 and so f
(
t
)
≤
t
n
/
(
n
-
α
) and 0
≤
-
g
′
(
t
)
/
g
(
t
)
≤
α
/
t. So
(89)
1
t
∫
0
t
‍
Y
~
s
d
s
=
1
t
∫
f
(
0
)
f
(
t
)
‍
Y
u
d
u
(
f
′
∘
f
-
1
(
u
)
)
3
/
2
and forT
=
f
(
t
), the mean |
(
1
/
T
)
∫
0
T
‍
Y
u
d
u
| is almost surely upper bounded by f
(
t
)
/
t
(
f
′
(
t
)
)
3
/
2
β
(
t
). As
(90)
1
β
(
t
)
=
f
′
(
t
)
f
(
t
)
+
f
′′
(
t
)
2
f
(
t
)
≤
(
g
∘
f
(
t
)
)
-
1
/
n
f
+
α
n
f
(
t
)
(
g
∘
f
(
t
)
)
2
/
n
≤
1
f
(
t
)
(
g
∘
f
(
t
)
)
1
/
n
,
we find that(91)
f
(
t
)
t
(
f
′
(
t
)
)
3
/
2
β
(
t
)
≤
f
(
t
)
(
g
∘
f
(
t
)
)
1
/
2
n
t
≤
(
g
∘
f
(
t
)
)
1
/
2
n
t
(
n
/
2
(
n
-
α
)
)
-
1
.This last term goes to 0 if n
>
2
α. As α is arbitrary chosen between 0 and 1, we conclude that |
(
1
/
T
)
∫
0
T
‍
Y
u
d
u
| converges almost surely to 0 for any n
≥
1.


## 4.2.1. Ift
g
(
t
) Goes to a Positive Constant

In this part, we suppose without any loss of generality thatt
g
(
t
)
=
1. Indeed, Y is solution to the SDE:
(76)
d
Y
t
=
d
B
t
-
1
t
(
∇
V
(
Y
t
)
+
Y
t
)
d
t
.Theorem 28.
Suppose that there existsn
≥
1 such that V
(
x
)
/
|
x
|
2
n converges to a positive constant and g
(
t
)
=
1
/
t. Then, X
t converges in distribution to Y
∞
+
μ
¯
∞ if and only if ∫
‍
x
e
-
2
V
(
x
)
d
x
=
0 and n
>
3
/
2. In that case, Y
∞ has the distribution law e
-
2
V
(
x
)
d
x (up to a positive multiplicative constant).Proof.
The proof is similar to the one of Theorem24. If V is polynomial or V
(
x
)
=
c
|
x
|
2
n (for c
>
0), then letting Y
~
t
:
=
(
(
n
-
1
)
/
n
)
t
-
1
/
2
(
n
-
1
)
Y
t
n
/
(
n
-
1
), we find that Y
~ satisfies
(77)
d
Y
~
t
=
d
B
t
-
(
2
c
n
|
Y
~
t
|
2
n
-
2
Y
~
t
+
2
n
+
1
2
(
n
-
1
)
t
Y
~
t
)
d
t
.
Now, we can approximate the potential V by c
|
x
|
2
n (for a well-chosen n
≥
1) such that the studied process Y
~ has the same asymptotic behaviour as
(78)
d
ξ
t
=
d
B
t
-
(
∇
V
(
ξ
t
)
+
2
n
+
1
2
(
n
-
1
)
t
ξ
t
)
d
t
in the sense of asymptotic pseudotrajectory. Actually, one easily shows that if ∫
x
e
-
2
V
(
x
)
d
x
=
0, then for all T
>
0
(79)
sup
⁡
0
≤
s
≤
T
|
Y
~
s
+
e
t
-
ξ
s
+
e
t
|
2
=
O
(
e
-
t
)
.
This has been already shown for Lemma 27 and we do not reproduce the proof here. Finally, this proves that Y
~ has the same ergodic behaviour as ξ (see [17]). So as ξ is ergodic and almost surely (
1
/
t
)
∫
0
t
ξ
s
d
s converges, the limit points of (
1
/
t
)
∫
0
t
δ
Y
~
s
d
s are included into the set of the invariant measures of ξ, that is, e
-
2
V
(
x
)
d
x. So (
1
/
t
)
∫
0
t
Y
~
s
d
s goes to zero if and only if ∫
x
e
-
2
V
(
x
)
d
x
=
0. By (79), the asymptotic pseudotrajectory has a polynomial speed. Moreover, ξ converges to its invariant probability measure with an exponential speed. Thus, (
1
/
t
)
∫
0
t
‍
Y
~
s
d
s converges almost surely to zero with a polynomial speed of convergence (of the order of 1
/
t) if ∫
x
e
-
2
V
(
x
)
d
x
=
0 (and diverges otherwise). Now, remembering that Y
~
s
=
s
-
1
/
2
(
n
-
1
)
Y
s
n
/
(
n
-
1
), we conclude that
(80)
1
t
∫
0
t
Y
~
s
d
s
=
1
t
∫
0
t
n
/
(
n
-
1
)
Y
u
d
u
u
3
/
2
n
⟶
0
a
.
s
.
Indeed, we have
(81)
1
t
∫
0
t
n
/
(
n
-
1
)
Y
u
d
u
u
3
/
2
n
=
1
t
1
+
3
/
2
(
n
-
1
)
∫
0
t
n
/
(
n
-
1
)
Y
u
d
u
-
1
t
∫
0
t
n
/
(
n
-
1
)
u
-
1
-
3
/
2
n
∫
0
u
Y
s
d
s
d
u
=
O
(
t
-
1
)
a
.
s
.
And thus, (
1
/
T
)
∫
0
T
Y
u
d
u converges almost surely to 0 (with T
=
t
n
/
(
n
-
1
)) if and only if n
>
3
/
2. We then refer to Section 4.1 to obtain the convergence of the process X.We also remark that this result is coherent with the basic Ornstein-Uhlenbeck case.


## 4.2.2. Ift
g
(
t
) Goes to the Infinity

This study will be divided into two different cases. First, we suppose that there exists0
<
α
<
1 such that t
α
g
(
t
) goes to a positive constant. Whereas in the second case, t
α
g
(
t
) goes to the infinity for any 0
<
α
<
1 (this is for instance satisfied by g
(
t
)
=
1
/
log
⁡
⁡
t). The first part of the study is identical for the two cases and we will only divide the end of the study.Theorem 29.
Suppose that there existsn
≥
1 such that V
(
x
)
/
|
x
|
2
n converges to a positive constant.(1)
If there exists0
<
α
<
1 such that g
(
t
)
=
t
α, then X
t converges in distribution to Y
∞
+
μ
¯
∞ if and only if ∫
x
e
-
2
V
(
x
)
d
x
=
0 and n
>
4
α.
(2)
Ift
α
g
(
t
)
→
+
∞ for all 0
<
α
<
1, then X
t converges in distribution to Y
∞
+
μ
¯
∞ if and only if ∫
x
e
-
2
V
(
x
)
d
x
=
0.Let us sketch the proof of Theorem29 that is postponed to the end of the paragraph. As in the preceding paragraph, we first suppose that V
(
x
)
=
c
|
x
|
2
n. Define f as the positive increasing solution to g
∘
f
(
t
)
=
(
f
′
(
t
)
)
-
n. Consider the time and scale-changed process Y
~ defined by Y
~
t
:
=
Y
f
(
t
)
/
f
′
(
t
). Applying Itô's formula to Y
~, we thus find that Y
~ satisfies the SDE:
(82)
d
Y
~
t
=
d
B
t
-
(
2
n
c
Y
~
t
|
Y
~
t
|
2
n
-
2
+
(
f
′
(
t
)
f
(
t
)
+
f
′′
(
t
)
2
f
′
(
t
)
)
Y
~
t
)
d
t
.
Now, we approximate the potential V by |
x
|
2
n for a well-chosen n
≥
1. So the studied process Y
~ has the same asymptotic behaviour as
(83)
d
ξ
t
=
d
B
t
-
(
∇
V
(
ξ
t
)
+
ξ
t
β
(
t
)
)
d
t
,
where β
(
t
) is defined by 1
/
β
(
t
)
=
f
′
(
t
)
/
f
(
t
)
+
f
′′
(
t
)
/
2
f
′
(
t
) (this last quantity goes to 0 as t tends to +
∞).Define also the processY
^ as the solution to the SDE
(84)
d
Y
^
t
=
d
B
t
-
∇
V
(
Y
^
t
)
d
t
.Lemma 30.
The processξ (and also Y
~) is an asymptotic pseudotrajectory for the process Y
^: for all t
,
T
>
0, we have sup
⁡
0
≤
s
≤
T
|
Y
^
s
+
t
-
ξ
s
+
t
|
=
O
(
β
(
t
)
-
1
/
2
) almost surely.Proof.
Lett
>
0 and 0
≤
s
≤
T. Itô's formula implies that
(85)
1
2
d
d
t
|
Y
^
s
+
t
-
ξ
s
+
t
|
2
=
-
(
∇
V
(
Y
^
s
+
t
)
-
∇
V
(
ξ
s
+
t
)
,
Y
^
s
+
t
-
ξ
s
+
t
)
+
1
β
(
s
+
t
)
(
ξ
s
+
t
,
Y
^
s
+
t
-
ξ
s
+
t
)
≤
-
(
C
W
-
C
χ
)
|
Y
^
s
+
t
-
ξ
s
+
t
|
2
+
1
2
β
(
s
+
t
)
|
Y
^
s
+
t
|
2
.
So we find that
(86)
|
Y
^
s
+
t
-
ξ
s
+
t
|
2
≤
e
-
2
(
C
W
-
C
χ
)
s
∫
0
s
e
(
C
W
-
C
χ
)
u
|
Y
^
u
+
t
|
2
d
u
β
(
u
+
t
)
.
Let us note π
=
∫
‍
e
-
2
V
(
x
)
d
x the normalisation constant. The ergodicity of Y
^ implies the existence of C
>
0 such that
(87)
∫
0
s
e
(
C
W
-
C
χ
)
u
|
Y
^
u
+
t
|
2
d
u
β
(
u
+
t
)
≤
1
β
(
t
)
{
|
∫
0
s
e
(
C
W
-
C
χ
)
u
(
|
Y
^
u
+
t
|
2
-
∫
|
x
|
2
e
-
2
V
(
x
)
π
d
x
‍
)
d
u
‍
|
+
e
(
C
W
-
C
χ
)
s
C
W
-
C
χ
∫
|
x
|
2
e
-
2
V
(
x
)
π
d
x
‍
}
≤
C
β
(
t
)
e
(
C
W
-
C
χ
)
s
∫
|
x
|
2
e
-
2
V
(
x
)
d
x
‍
.
This leads to the result. And also there exists a constant M
>
0 such that almost surely
(88)
sup
⁡
0
≤
s
≤
T
|
Y
^
s
+
t
-
ξ
s
+
t
|
2
≤
M
β
(
t
)
.Proof of Theorem29.
Lemma30 proves that (
1
/
t
)
∫
0
t
‍
Y
~
u
d
u converges almost surely to 0 if and only ∫
‍
x
e
-
2
V
(
x
)
d
x
=
0. Moreover, we see by a similar equation (88) that Y
~ is an asymptotic pseudotrajectory for Y
^ with the speed of convergence 1
/
β
(
t
). As Y
^ converges to its invariant probability measure e
-
2
V
(
x
)
d
x with an exponential speed of convergence, we find that (
1
/
t
)
∫
0
t
‍
Y
~
s
d
s converges almost surely to 0 if and only if ∫
‍
x
e
-
2
V
(
x
)
d
x
=
0 and in that case, we have the following result, depending on the function g.(1)
First, suppose thatg
(
t
)
=
t
-
α for a given 0
<
α
<
1. We thus have f
(
t
)
=
t
n
/
(
n
-
α
) and (
1
/
t
)
∫
0
t
‍
Y
s
n
/
(
n
-
α
)
(
d
s
/
s
α
/
2
(
n
-
α
)
)
=
(
1
/
t
)
∫
0
t
n
/
(
n
-
α
)
‍
Y
u
(
d
u
/
u
3
α
/
2
n
) converge to 0 if and only if ∫
‍
x
e
-
2
V
(
x
)
d
x
=
0. Then, almost surely |
(
1
/
T
)
∫
0
T
‍
Y
u
d
u
| behaves asymptotically as T
(
2
α
/
n
)
-
(
1
/
2
), where T
=
t
n
/
(
n
-
α
) and thus it converges to zero if and only if n
>
4
α.
(2)
Suppose now thatt
α
g
(
t
)
→
∞ for all 0
<
α
<
1. This implies that g
(
t
)
≥
t
-
α for any 0
<
α
<
1 and so f
(
t
)
≤
t
n
/
(
n
-
α
) and 0
≤
-
g
′
(
t
)
/
g
(
t
)
≤
α
/
t. So
(89)
1
t
∫
0
t
‍
Y
~
s
d
s
=
1
t
∫
f
(
0
)
f
(
t
)
‍
Y
u
d
u
(
f
′
∘
f
-
1
(
u
)
)
3
/
2
and forT
=
f
(
t
), the mean |
(
1
/
T
)
∫
0
T
‍
Y
u
d
u
| is almost surely upper bounded by f
(
t
)
/
t
(
f
′
(
t
)
)
3
/
2
β
(
t
). As
(90)
1
β
(
t
)
=
f
′
(
t
)
f
(
t
)
+
f
′′
(
t
)
2
f
(
t
)
≤
(
g
∘
f
(
t
)
)
-
1
/
n
f
+
α
n
f
(
t
)
(
g
∘
f
(
t
)
)
2
/
n
≤
1
f
(
t
)
(
g
∘
f
(
t
)
)
1
/
n
,
we find that(91)
f
(
t
)
t
(
f
′
(
t
)
)
3
/
2
β
(
t
)
≤
f
(
t
)
(
g
∘
f
(
t
)
)
1
/
2
n
t
≤
(
g
∘
f
(
t
)
)
1
/
2
n
t
(
n
/
2
(
n
-
α
)
)
-
1
.This last term goes to 0 if n
>
2
α. As α is arbitrary chosen between 0 and 1, we conclude that |
(
1
/
T
)
∫
0
T
‍
Y
u
d
u
| converges almost surely to 0 for any n
≥
1.



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*Source: 364321-2014-04-15.xml*