2.1. Estimate of the errors of general SG-LMC

We consider a compact polynomial mollifier [Reference Anderson3] $\rho:\mathbb{R}^{d}\to[0,\infty)$ of class $\mathcal{C}^{1}$ as follows:

(4) \begin{align} \rho(x)=\begin{cases} \left(\frac{\pi^{d/2}\text{B}(d/2,3)}{\Gamma(d/2)}\right)^{-1}\left(1-|x|^{2}\right)^{2}&\text{if }|x|\le 1,\\[5pt] 0 & \text{otherwise,} \end{cases}\end{align}

where $\text{B}(\cdot,\cdot)$ is the beta function and $\Gamma(\! \cdot \!)$ is the gamma function. Note that $\nabla \rho$ has an explicit $L^{1}$ -bound, which is the reason to adopt $\rho$ as the mollifier in our analysis; we give more detailed discussions on $\rho$ in Section 4.2. Let $\rho_{r}(x)=r^{-d}\rho(x/r)$ with $r>0$ .

We define $\widetilde{G}(x)$ such that for each $x\in\mathbb{R}^{d}$ ,

\begin{align*} \widetilde{G}(x)\,:\!=\,E\left[{G}\left(x,a_{0}\right)\right],\end{align*}

whose measurability is given by Tonelli’s theorem.

We set the following assumptions on U and G.

1. (A1) $U\in W_{\text{loc}}^{1,\infty}(\mathbb{R}^{d})$ .

2. (A2) For each $a\in A$ and $x\in\mathbb{R}^{d}$ , $|G(x,a)|<\infty$ .

Under (A1), we fix a representative $\nabla U$ and consider the assumptions on $\nabla U$ and $\widetilde{G}$ .

1. (A3) $|\nabla U(\mathbf{0})|<\infty$ and $|\widetilde{G}(\mathbf{0})|<\infty$ , and the moduli of continuity of $\nabla U$ and $\widetilde{G}$ are bounded, that is,

   \begin{align*} \omega_{\nabla U}(r)&\,:\!=\,\sup\nolimits_{x,y\in\mathbb{R}^{d}:|x-y|\le r}\left|\nabla U(x)-\nabla U(y)\right|<\infty,\\ \omega_{\widetilde{G}}(r)&\,:\!=\,\sup\nolimits_{x,y\in\mathbb{R}^{d}:|x-y|\le r}\left|\widetilde{G}(x)-\widetilde{G}(y)\right|<\infty \end{align*}
   for some $r\in(0,1]$ .

2. (A4) For some $m,\tilde{m},b,\tilde{b}>0$ , for all $x\in\mathbb{R}^{d}$ ,

   \begin{align*} \left\langle x,\nabla U\left(x\right)\right\rangle \ge m\left|x\right|^{2}-b,\ \left\langle x,\widetilde{G}\left(x\right)\right\rangle \ge \tilde{m}\left|x\right|^{2}-\tilde{b}. \end{align*}

Under (A1) and (A3), we can define the mollification

\begin{align*} \nabla \bar{U}_{r}(x)\,:\!=\,\nabla \left(U\ast \rho_{r}\right)(x)=\nabla \int_{\mathbb{R}^{d}} U\left(y\right)\rho_{r}\left(x-y\right)\text{d} y=\left(\nabla U\ast \rho_{r}\right)(x),\end{align*}

where the last equality holds since (A1) gives the essential boundedness of U and $\nabla U$ on any compact sets and we can approximate $\rho_{r}\in C_{0}^{1}(\mathbb{R}^{d})\cap W^{1,1}(\mathbb{R}^{d})$ with some $\{\varphi_{n}\}\subset\mathcal{C}_{0}^{\infty}(\mathbb{R}^{d})$ . Note that $\bar{U}_{r}\in \mathcal{C}^{2}(\mathbb{R}^{d})$ with $\nabla^{2}\bar{U}_{r}=\nabla U\ast \nabla\rho_{r}$ by this discussion (see Lemma 4.7). We assume quadratic growth of the bias of G with respect to $\nabla \bar{U}_{r}$ and the variance as well.

1. (A5) For some $\bar{\delta}>0$ and $\delta_{r}\,:\!=\,(\delta_{\mathbf{b},r,0},\delta_{\mathbf{b},r,2},\delta_{\mathbf{v},0},\delta_{\mathbf{v},2})\in[0,\bar{\delta}]^{4}$ , for almost all $x\in\mathbb{R}^{d}$ ,

   \begin{align*} \left|\widetilde{G}(x)-\nabla \bar{U}_{r}(x)\right|^{2}&\le 2\delta_{\mathbf{b},r,2}\left|x\right|^{2}+2\delta_{\mathbf{b},r,0},\\ E\left[\left|{G}\left(x,a_{0}\right)-\widetilde{G}(x)\right|^{2}\right]&\le 2\delta_{\mathbf{v},2}\left|x\right|^{2}+2\delta_{\mathbf{v},0}. \end{align*}

For brevity, we use the notation $\delta_{r,i}=\delta_{\mathbf{b},r,i}+\delta_{\mathbf{v},i}$ for both $i=0,2$ . We also give the condition on the initial value $\xi$ .

1. (A6) The initial value $\xi$ has the law $\mu_{0}(\text{d} x)=\left(\int_{\mathbb{R}^{d}} \exp(-\Psi(x))\text{d} x\right)^{-1}\exp(\! - \! \Psi(x))\text{d} x$ with $\Psi:\mathbb{R}^{d}\to[0,\infty)$ and $\psi_{0},\psi_{2}>0$ such that $(2\vee \beta(|\nabla U(\mathbf{0})|+\omega_{\nabla U}(1)))|x|^{2}-\psi_{0}\le \Psi(x)\le \psi_{2}|x|^{2}+\psi_{0}$ for all $x\in\mathbb{R}^{d}$ .

Assumption (A6) yields

\begin{align*} \kappa_{0}\,:\!=\,\log\int_{\mathbb{R}^{d}}\mathrm{e}^{|x|^{2}}\mu_{0}(\text{d} x)<\infty.\end{align*}

Let $\mu_{t}$ , $t\ge0$ , denote the probability measure of $Y_{t}$ . The following theorem gives an upper bound for the 2-Wasserstein distance between $\mu_{k\eta}$ and $\pi$ ; its proof is given in Section 5.

Theorem 2.1 (Error estimate of general SG-LMC.) Assume (A1)–(A6) and $\eta\in(0,1\wedge(\tilde{m}/2((\omega_{\widetilde{G}}(1))^{2}+\delta_{\mathbf{v},2}))]$ . Then for any $r\in(0,1]$ and $k\in\mathbb{N}$ ,

\begin{align*} \mathcal{W}_{2}\left(\mu_{k\eta},\pi\right)\le &2C_{1}\left.\left(x^{1/2}+ x^{1/4}\right)\right|_{x=f(\delta_{r},r,k,\eta)}+\sqrt{2C_{2}c_\textrm{P}(\bar{\pi}_{r})}\mathrm{e}^{-k\eta/2\beta c_\textrm{P}\left(\bar{\pi}_{r}\right)}, \end{align*}

where f is the function defined as

\begin{align*} f(\delta_{r},r,k,\eta)\,:\!=\,\left(C_{0}\frac{\omega_{\nabla U}(r)}{r}\eta+\beta\left(\delta_{r,2}\kappa_{\infty}+\delta_{r,0}\right)\right)k\eta+\beta r\omega_{\nabla U}(r), \end{align*}

$C_{0},C_{1},C_{2},\kappa_{\infty}>0$ are the positive constants defined as

\begin{align*} C_{0}&\,:\!=\,\left(d+2\right)\left(\frac{\beta}{3}\left(\left\|\widetilde{G}\right\|_{\mathbb{M}}^{2}+\delta_{\mathbf{v},0}+\left((\omega_{\widetilde{G}}(1))^{2}+\delta_{\mathbf{v},2}\right)\kappa_{\infty}\right)+\frac{d}{2}\right),\\ C_{1}&\,:\!=\,2\sqrt{4\kappa_{0}+\frac{16(\bar{b}+d/\beta)}{m}+\frac{10}{1\wedge (\beta m/4)}},\\ C_{2}&\,:\!=\,3^{\beta b/2}\left(\frac{3\psi_{2}}{m\beta}\right)^{d/2}\exp\left(\beta\left(2\left\|\nabla U\right\|_{\mathbb{M}}+U_{0}\right)+2\psi_{0}\right),\\ \kappa_{\infty}&\,:\!=\,\kappa_{0}+2\left(1\vee \frac{1}{\tilde{m}}\right)\left(\tilde{b}+\left\|\widetilde{G}\right\|_{\mathbb{M}}^{2}+\delta_{\mathbf{v},0}+\frac{d}{\beta}\right), \end{align*}

$\bar{b}\,:\!=\,b+\omega_{\nabla U}(1)$ , $U_{0}\,:\!=\,\left\|U\right\|_{L^{\infty}(B_{1}(\mathbf{0}))}$ , $\|\nabla U\|_{\mathbb{M}},\|\widetilde{G}\|_{\mathbb{M}}>0$ are the positive constants defined as

\begin{align*} \left\|\nabla U\right\|_{\mathbb{M}}\,:\!=\,\left|\nabla U(\mathbf{0})\right|+\omega_{\nabla U}(1),\quad \left\|\widetilde{G}\right\|_{\mathbb{M}}\,:\!=\,\left|\widetilde{G}(\mathbf{0})\right|+\omega_{\widetilde{G}}(1), \end{align*}

$c_\textrm{P}(\bar{\pi}^{r})>0$ is the constant of a Poincaré inequality for $\bar{\pi}^{r}(\text{d} x)\propto \exp(\! - \! \beta\bar{U}_{r}(x))\text{d} x$ such that

\begin{align*} c_\textrm{P}(\bar{\pi}^{r}) &\le \frac{2}{m\beta\left(d+\bar{b}\beta\right)}+\frac{4a\left(d+\bar{b}\beta\right)}{m\beta}\exp\left(\beta\left(\frac{3}{2}\left\|\nabla U\right\|_{\mathbb{M}}\left(1+\frac{4\left(d+\bar{b}\beta\right)}{m\beta}\right)+U_{0}\right)\right), \end{align*}

and $a>0$ is a positive absolute constant.

2.2. Concise representation of Theorem 2.1

Since the constants and the upper bounds for some of them in Theorem 2.1 depend on various parameters, we give a concise representation of the result for the error analyses below. Assume that $f(\delta_{r},r,k,\eta)\le 1$ and $\eta\in(0,1\wedge (\tilde{m}/2((\omega_{\widetilde{G}}(1))^{2}+\delta_{\mathbf{v},2}))]$ and note that Lemma 4.10 and the perturbation theory [Reference Bakry, Gentil and Ledoux5] yield that $\exp(\! -2 \! \omega_{\nabla U}(1))c_\textrm{P}(\bar{\pi}^{r})\le c_{\text{P}}(\pi)\le \exp(2\omega_{\nabla U}(1))c_\textrm{P}(\bar{\pi}^{r})$ for any $r\in(0,1]$ , where $c_{\text{P}}(\pi)$ is the Poincaré constant of $\pi$ . We then obtain that for some $C\ge 1$ independent of $\delta_{r},r,k,\eta,d,c_{\text{P}}(\pi)$ ,

\begin{align*} \mathcal{W}_{2}\left(\mu_{k\eta},\pi\right)&\le C\sqrt{d}\sqrt[4]{\left(d^{2}\frac{\omega_{\nabla U}(r)}{r}\eta+d\delta_{r,2}+\delta_{r,0}\right)k\eta+r\omega_{\nabla U}(r)}\\ &\quad+\mathrm{e}^{Cd}\exp\left(-\frac{k\eta}{Cc_{\text{P}}(\pi)}\right).\end{align*}

1. (i) If U is $\lambda$ -strongly convex with $\lambda>0$ , then $c_{\text{P}}(\pi)\le 1/(\beta\lambda)$ .

2. (ii) (Perturbation theory; see [Reference Bakry, Gentil and Ledoux5]) If $U=F+V$ with essentially bounded F and $\lambda$ -strongly convex V with $\lambda>0$ , then $c_{\text{P}}(\pi)\le \exp(2\beta \|F\|_{L^{\infty}})/(\beta\lambda)=\mathcal{O}(1)$ .

3. (iii) (Miclo’s trick; see [Reference Bardet, Gozlan, Malrieu and Zitt6]) If $U=U_{l}+U_{c}$ with M-Lipschitz continuous $U_{l}$ with $M>0$ and $\lambda$ -strongly convex $V\in\mathcal{C}^{2}$ with $\lambda>0$ , then $c_{\text{P}}(\pi)\le (4/(\beta\lambda))\exp(4\beta M^{2}\sqrt{2d}/(\lambda\sqrt{\pi}))=\mathcal{O}(\exp(\mathcal{O}(\sqrt{d})))$ .

2.3. Discussion on Theorem 2.1

We note a potential direction to improve the bound. Some recent studies (e.g., [Reference Altschuler and Talwar1, Reference Vempala and Wibisono33]) directly evaluate the divergence of algorithms from the target distribution or their invariant distributions and successfully yield tight bounds. Hence, extension of their analysis to our problem can improve our results; for instance, if we obtain a tight bound for the Kullback–Leibler divergence of SG-LMC from $\pi$ , and $\pi$ satisfies a logarithmic Sobolev inequality, then we can combine them with Talagrand’s transportation-entropy inequality and yield a tighter bound for the 2-Wasserstein distance. To extend the method of [Reference Vempala and Wibisono33], which is suitable for our non-convex set-up, we need to rigorously validate the exchange of limits and integration by parts. We leave it as an open problem as its reasonable sufficient conditions are not very obvious (in particular, we need to examine the growth of $E[G(Y_{\lfloor t/\eta\rfloor\eta},a_{\lfloor t/\eta\rfloor\eta})|Y_{t}]$ and the density of the law of $Y_{t}$ in $|Y_{t}|$ or sufficient conditions on $a_{i\eta}$ to control their growth).

Our proof strategy, taken mainly from [Reference Raginsky, Rakhlin and Telgarsky31], successfully avoids the technical difficulty appearing in the direct comparison. We obtain our bound by the triangular inequality for the 2-Wasserstein distance and separately evaluating the distance between (i) the laws of SG-LMC and a Langevin process, (ii) the law of the Langevin process and its invariant measure, and (iii) the invariant measure and target measure. A dependence on $f^{1/4}$ is inevitable as long as we compare the SG-LMC and the Langevin process via Kullback–Leibler divergence and use the weighted Csiszár–Kullback–Pinsker inequality for the 2-Wasserstein distance [Reference Bolley and Villani7], which are tight for general purposes. This dependence would be largely relaxed if we can give a direct comparison of the algorithm with the target or invariant measure as in [Reference Vempala and Wibisono33] or [Reference Altschuler and Talwar1].

3.1. Analysis of the LMC algorithm for U of class $\mathcal{C}^{1}$ with uniformly continuous gradient

We examine the LMC algorithm for U with uniformly continuous gradient, that is, $\omega_{\nabla U}(r)\to 0$ as $r\to0$ . Under the LMC algorithm, we use $G=\nabla U$ and thus $\widetilde{G}=\nabla U$ . Therefore, the bias–variance decomposition in (A5) is given as $\delta_{\mathbf{b},r,0}=(\omega_{\nabla U}(r))^{2}/2$ , $\delta_{\mathbf{b},r,2}=\delta_{\mathbf{v},0}=\delta_{\mathbf{v},2}=0$ by Lemma 4.9 below.

In this section we make the following assumptions.

1. (B1) $U\in\mathcal{C}^{1}(\mathbb{R}^{d})$ .

2. (B2) $\nabla U$ is uniformly continuous, that is, the modulus of continuity $\omega_{\nabla U}$ defined as

   \begin{align*} \omega_{\nabla U}\left(r\right)\,:\!=\,\sup_{x,y\in\mathbb{R}^{d}:|x-y|\le r}\left|\nabla U(x)-\nabla U(y)\right|<\infty \end{align*}
   with $r\ge0$ is continuous at zero.

3. (B3) There exist $m,b>0$ such that for all $x\in\mathbb{R}^{d}$ ,

   \begin{align*} \left\langle x,\nabla U(x)\right\rangle \ge m\left|x\right|^{2}-b. \end{align*}

(A1)–(A4) hold immediately by (B1)–(B3); therefore, we obtain the following corollary.

Corollary 3.1. (Error estimate of LMC.) Under (B1)–(B3) and (A6), there exists a constant $C\ge1$ independent of $r,\alpha,k,\eta,d,c_{\text{P}}(\pi)$ such that for all $k\in\mathbb{N}$ , $\eta\in(0,1\wedge (m/2(\omega_{\nabla U}(1))^{2})]$ , and $r\in(0,1]$ with $\left(d^{2}(\omega_{\nabla U}(r)/r)\eta+(\omega_{\nabla U}(r))^{2}\right)k\eta+r\omega_{\nabla U}(r)\le 1$ ,

\begin{align*} \mathcal{W}_{2}\left(\mu_{k\eta},\pi\right)&\le C\sqrt{d}\sqrt[4]{\left(d^{2}\frac{\omega_{\nabla U}(r)}{r}\eta+\left(\omega_{\nabla U}\left(r\right)\right)^{2}\right)k\eta+r\omega_{\nabla U}(r)}\\ &\quad+\mathrm{e}^{Cd}\exp\left(-\frac{k\eta}{Cc_{\text{P}}(\pi)}\right).\end{align*}

3.1.1. The sampling complexity of the LMC algorithm.

We present propositions regarding the sampling complexity to achieve the approximation $\mathcal{W}_{2}(\mu_{k\eta},\pi)\le \epsilon$ for arbitrary $\epsilon>0$ . Define generalized inverses of $\omega_{\nabla U}$ as follows: for any $s>0$ ,

\begin{align*} \omega_{\nabla U}^{\dagger}\left(s\right)\,:\!=\,\sup\left\{r\ge 0:\omega_{\nabla U}(r)\le s\right\}.\end{align*}

The continuity of $\nabla U$ under (B2) along with its monotonicity gives $\omega_{\nabla U}(\omega_{\nabla U}^{\dagger}(s))= s$ . We also define a generalized inverse of $r\omega_{\nabla U}(r)$ such that for all $s>0$ ,

\begin{align*} \iota\left(s\right)\,:\!=\,\sup\left\{r\ge 0:r\omega_{\nabla U}(r)\le s\right\}.\end{align*}

The following proposition yields the sampling complexity using this generalized inverse.

Proposition 3.1. Assume that (B1)–(B3) and (A6) hold and fix $\epsilon\in(0,1]$ . We set $\bar{r}_{1},\bar{r}_{2}>0$ such that

\begin{align*} \bar{r}_{1}\,:\!=\,\omega_{\nabla U}^{\dagger}\left(\sqrt{\frac{\epsilon^{4}}{48C^{4}d^{2}\left(Cc_{\text{P}}(\pi)\left(\log\left(2/\epsilon\right)+Cd\right)+1\right)}}\right),\quad \bar{r}_{2}\,:\!=\,\iota\left(\frac{\epsilon^{4}}{48C^{4}d^{2}}\right). \end{align*}

If $r= \bar{r}_{1}\wedge \bar{r}_{2}$ and

\begin{align*} \eta\le 1&\wedge \frac{m}{2\left(\omega_{\nabla U}(1)\right)^{2}}\wedge \left(\frac{r}{\omega_{\nabla U}(r)}\frac{\epsilon^{4}}{48C^{4}d^{4}\left(Cc_{\text{P}}(\pi)\left(\log\left(2/\epsilon\right)+Cd\right)+1\right)}\right), \end{align*}

then $\mathcal{W}_{2}\left(\mu_{k\eta},\pi\right)\le \epsilon$ with $k=\lceil Cc_{\text{P}}(\pi)\left(\log(2/\epsilon)+Cd\right)/\eta\rceil$ .

Proof. We just need to confirm

\begin{align*} \max\left\{d^{2}\frac{\omega_{\nabla U}\left(r\right)}{r}k\eta^{2},\left(\omega_{\nabla U}\left(r\right)\right)^{2}k\eta,r\omega_{\nabla U}\left(r\right)\right\}\le \frac{\epsilon^{4}}{48C^{4}d^{2}},\quad \mathrm{e}^{Cd}\exp\left(-\frac{k\eta}{Cc_{\text{P}}(\pi)}\right)\le \frac{\epsilon}{2}.\end{align*}

$r\omega_{\nabla U}\left(r\right)\le \epsilon^{4}/48C^{4}d^{2}$ is immediate. Since $\eta\le 1$ , we have

\begin{align*} Cc_{\text{P}}(\pi)\left(\log\left(2/\epsilon\right)+Cd\right)\le k\eta\le Cc_{\text{P}}(\pi)\left(\log\left(2/\epsilon\right)+Cd\right)+1,\end{align*}

and the other bounds also hold.

We can apply Proposition 3.1 to analysis of the sampling complexity of LMC with $\alpha$ -mixture weakly smooth gradients [Reference Chatterji, Diakonikolas, Jordan and Bartlett10, Reference Nguyen29]. Assume that there exist $M>0$ and $\alpha\in(0,1]$ such that for all $x,y\in\mathbb{R}^{d}$ ,

\begin{align*} \left|\nabla U(x)-\nabla U(y)\right|\le M\left(\left|x-y\right|^{\alpha}\vee\left|x-y\right|\right),\end{align*}

which is a weaker assumption than both $\alpha$ -Hölder continuity and Lipschitz continuity. This allows the gradient $\nabla U(x)$ to be at most of linear growth, while $\alpha$ -Hölder continuity with $\alpha\in(0,1)$ lets the gradient be at most of sublinear growth. Since $\omega_{\nabla U}(r)\le M(r^{\alpha}\vee r)$ for all $r\ge0$ , we have $\omega_{\nabla U}^{\dagger}(s)\ge (s/M)^{1/\alpha}$ for $s\in(0,1/M]$ . Rough estimates of $r/\omega_{\nabla U}(r)$ by the inequalities $r/\omega_{\nabla U}(r)\ge r^{1-\alpha}/M$ , $\omega_{\nabla U}^{\dagger}(s)/\omega_{\nabla U}(\omega_{\nabla U}^{\dagger}(s))= \omega_{\nabla U}^{\dagger}(s)/s\ge s^{1/\alpha-1}/M^{1/\alpha}$ , $\iota(s)\ge(s/M)^{1/(1+\alpha)}$ , and $\iota(s)/\omega_{\nabla U}(\iota(s))\ge \iota(s)^{1-\alpha}/M\ge s^{(1-\alpha)/(1+\alpha)}/M^{2/(1+\alpha)}$ for sufficiently small $r,s>0$ yield the sampling complexity

\begin{align*} k=\mathcal{O}\left(\frac{d^{4}c_{\text{P}}(\pi)^{2}\left(\log\epsilon^{-1}+d\right)^{2}}{\epsilon^{4}}\left(\left(\frac{d^{2}c_{\text{P}}(\pi)\left(\log\epsilon^{-1}+d\right)}{\epsilon^{4}}\right)^{\frac{1-\alpha}{2\alpha}}\vee \left(\frac{d^{2}}{\epsilon^{4}}\right)^{\frac{1-\alpha}{1+\alpha}}\right)\right).\end{align*}

3.2. The spherically smoothed Langevin Monte Carlo algorithm

We consider a stochastic gradient G unbiased for $\nabla \bar{U}_{r}$ with fixed $r\in(0,1]$ such that the sampling error can be sufficiently small.

Note that $\rho$ is the density of random variables which we can generate as a product of a random variable following the uniform distribution on $S^{d-1}=\{x\in\mathbb{R}^{d} \, : \, |x|=1\}$ and the square root of a random variable following the beta distribution $\textrm{Beta}(d/2,3)$ independently. Therefore, we can consider spherical smoothing with the random variables whose density is $\rho_{r}$ as an analogue to Gaussian smoothing of [Reference Chatterji, Diakonikolas, Jordan and Bartlett10].

Set the stochastic gradient

\begin{align*} {G}\left(x,a_{i\eta}\right)=\frac{1}{N_{B}}\sum_{j=1}^{N_{B}}\nabla U\left(x+r'\zeta_{i,j}\right),\end{align*}

where $N_{B}\in\mathbb{N}$ , $r'\in(0,1]$ , $a_{i\eta}=[\zeta_{i,1},\ldots,\zeta_{i,N_{B}}]$ , and $\{\zeta_{i,j}\}$ is a sequence of i.i.d. random variables with the density $\rho$ . Then for any $x\in\mathbb{R}^{d}$ , $E[{G}(x,a_{i\eta})]=\nabla \bar{U}_{r'}(x)$ ,

\begin{align*} &E\left[\left|{G}\left(x,a_{i\eta}\right)-\nabla \bar{U}_{r'}(x)\right|^{2}\right]\\ &\quad=\frac{1}{N_{B}}\int _{\mathbb{R}^{d}}\left|\nabla U(x-y)-\nabla \bar{U}_{r'}(x)\right|^{2}\rho_{r'}(y)\text{d} y\\ &\quad\le \frac{1}{N_{B}}\int_{\mathbb{R}^{d}}\int_{\mathbb{R}^{d}} \left|\nabla U(x-y)-\nabla U(x-z)\right|^{2}\rho_{r'}(y)\rho_{r'}(z)\text{d} y\text{d} z\\ &\quad\le \frac{1}{N_{B}}\int_{\mathbb{R}^{d}}\int_{\mathbb{R}^{d}} \left(\left|\nabla U(x-y)-\nabla U(x)\right|+\left|\nabla U(x-z)-\nabla U(x)\right|\right)^{2}\rho_{r'}(y)\rho_{r'}(z)\text{d} y\text{d} z\\ &\quad\le \frac{\left(2\omega_{\nabla U}(r')\right)^{2}}{N_{B}}\end{align*}

by Jensen’s inequality, and (A5) holds if $\nabla \bar{U}_{r'}(x)$ is well defined and $\omega_{\nabla U}(r')<\infty$ exists.

The main idea is to let $r'=r$ , where r is the radius of the implicit mollification and r’ is the radius of the support of the random noise which we control. Hence, the stochastic gradient G with $r'=r$ is an unbiased estimator of the mollified gradient $\nabla \bar{U}_{r}(x)$ . We call the algorithm with this G the spherically smoothed Langevin Monte Carlo algorithm. We can control the sampling error of SS-LMC to be close to zero by taking a sufficiently small r.

3.2.1. Regularity conditions.

Let us set the following assumptions.

1. (C1) $U\in W_{\text{loc}}^{1,\infty}(\mathbb{R}^{d})$ .

2. (C2) $|\nabla U(\mathbf{0})|<\infty$ and the modulus of continuity of $\nabla U$ is bounded, that is,

   \begin{align*} \omega_{\nabla U}(r)\,:\!=\,\sup\nolimits_{x,y\in\mathbb{R}^{d}:|x-y|\le r}\left|\nabla U(x)-\nabla U(y)\right|<\infty \end{align*}
   for some $r\in(0,1]$ .

3. (C3) There exist $m,b>0$ such that for all $x\in\mathbb{R}^{d}$ ,

   \begin{align*} \left\langle x,\nabla U\left(x\right)\right\rangle &\ge m\left|x\right|^{2}-b. \end{align*}

Let us observe that (C1)–(C3) yield (A1)–(A5). Assumption (A1) is the same as (C1). Assumption (C2) yields (A2) by Lemma 4.4 and (A3) by $|\nabla \bar{U}_{r}(\mathbf{0})|\le |\nabla U(\mathbf{0})|+\omega_{\nabla U}(1)$ and $\omega_{\nabla \bar{U}_{r}}(1)\le 3\omega_{\nabla U}(1)<\infty$ . Assumption (A4) also holds since

\begin{align*} \left\langle x,\nabla \bar{U}_{r}\left(x\right)\right\rangle&\ge m|x|^{2}-(b+\omega_{\nabla U}(1));\end{align*}

Section 5 gives the detailed derivation of this inequality. Assumption (A5) is given by (C2) and the discussion above.

3.2.2. Examples of distributions with the regularity conditions.

We show a simple class of potential functions satisfying (C1)–(C3) and some examples in Bayesian inference; assume $\beta=1$ for simplicity of interpretation. Let us consider a possibly non-convex loss with elastic net regularization such that

\begin{align*} U\left(x\right)=L\left(x\right)+\frac{\lambda_{1}}{\sqrt{d}}R_{1}\left(x\right)+\lambda_{2}R_{2}\left(x\right),\end{align*}

where $L \, : \, \mathbb{R}^{d}\to[0,\infty)$ is in $W_{\text{loc}}^{1,\infty}(\mathbb{R}^{d})$ with a weak gradient $\nabla L$ satisfying $\|\nabla L\|_{\infty}<\infty$ , $\lambda_{1}\ge 0$ , $\lambda_{2}>0$ , $R_{1}(x)=\sum_{i=1}^{d}|x^{(i)}|$ with $x^{(i)}$ indicating the ith component of x, and $R_{2}(x)=|x|^{2}$ . Fix a weak gradient of $R_{1}$ as $\nabla R_{1}(x)=\left(\text{sgn}(x^{(1)}),\ldots,\text{sgn}(x^{(d)})\right)$ ; then $\omega_{\nabla U}(1)\le 2(\|\nabla L\|_{\infty}+\lambda_{1}+\lambda_{2})<\infty$ and $\langle x,\nabla U(x)\rangle\ge \lambda_{2}|x|^{2}-\|\nabla L\|_{\infty}^{2}/4\lambda_{2}$ since $\langle x,\nabla R_{1}(x)\rangle \ge 0$ for all $x\in\mathbb{R}^{d}$ . Note that regularization corresponds to the potentials of prior distributions in Bayesian inference; we can regard L(x) as a negative quasi-log-likelihood and $\lambda_{1}/\sqrt{d}R_{1}(x)+\lambda_{2}R_{2}$ as a combination of Laplace prior and Gaussian prior [Reference Cui, Tong and Zahm12].

Non-convex losses with bounded weak gradients often appear in nonlinear and robust regression. We first examine a squared loss for nonlinear regression (or equivalently nonlinear regression with Gaussian errors) such that $L_\textrm{NLR}(x)=\frac{1}{2\sigma^{2}}\sum_{\ell=1}^{N}\left(y_{\ell}-\phi_{\ell}\left(x\right)\right)^{2}$ , where $N\in\mathbb{N}$ , $\sigma>0$ is fixed, $y_{\ell}\in\mathbb{R}$ , and $\phi_{\ell}\in W_{\text{loc}}^{1,\infty}(\mathbb{R}^{d})$ with $\|\phi_{\ell}\|_{\infty}+\|\nabla \phi_{\ell}\|_{\infty}<\infty$ for some $\nabla \phi_{\ell}$ (e.g., a two-layer neural network with clipped rectified linear unit activation such that $\phi_{\ell}(x)=(1/W)\sum_{w=1}^{W}a_{w}\varphi_{[0,c]}(\langle x_{w},f_{\ell}\rangle)$ , where $\varphi_{[0,c]}(t)=(0\vee t)\wedge c$ with $t\in\mathbb{R}$ , $a_{w}\in\{-1,1\}$ and $c>0$ are fixed, $f_{\ell}\in\mathbb{R}^{F}$ , $x=(x_{1},\ldots,x_{W})\in \mathbb{R}^{FW}$ , $F,W\in\mathbb{N}$ , and $d=FW$ ). This $L_\textrm{NLR}$ indeed satisfies $\|\nabla L_\textrm{NLR}\|_{\infty}\le \sum_{\ell=1}^{N}(|y_{\ell}|+\|\phi_{\ell}\|_{\infty})\|\nabla \phi_{\ell}\|_{\infty}/\sigma^{2}<\infty$ . Another example is a Cauchy loss for robust linear regression (or equivalently linear regression with Cauchy errors) such that $L_\textrm{RLR}(x)=\sum_{\ell=1}^{N}\log(1+|y_{\ell}-\langle f_{\ell},x\rangle|^{2}/\sigma^{2})$ , where $N\in\mathbb{N}$ , $\sigma>0$ is fixed, $y_{\ell}\in\mathbb{R}$ , and $f_{\ell}\in\mathbb{R}^{d}$ . The fact $|\frac{\text{d}}{\text{d} t}\log(1+t^{2}/\sigma^{2})|=|2t/(t^{2}+\sigma^{2})|\le 1/\sigma$ for all $t\in\mathbb{R}$ yields $\|\nabla L_\textrm{RLR}\|_{\infty}\le \sum_{\ell=1}^{N}|f_{\ell}|/\sigma<\infty$ .

3.2.3. Error estimate and sampling complexity of SS-LMC.

We now give our error estimate for SS-LMC.

Corollary 3.2. (Error estimate of SS-LMC.) Under (C1)–(C3) and (A6), there exists a constant $C\ge 1$ independent of $N_{B},r,k,\eta,d,c_{\text{P}}(\pi)$ such that for all $k\in\mathbb{N}$ , $\eta\in(0,1\wedge (m/(4(\omega_{\nabla U}(1))^{2}))]$ , $r\in(0,1]$ , and $N_{B}\in\mathbb{N}$ with $(d^{2}(\omega_{\nabla U}(r)/r)\eta+(\omega_{\nabla U}(r))^{2}/N_{B})k\eta+r\omega_{\nabla U}(r)\le 1$ ,

\begin{align*} \mathcal{W}_{2}\left(\mu_{k\eta},\pi\right)&\le C\sqrt{d}\sqrt[4]{\left(d^{2}\frac{\omega_{\nabla U}(r)}{r}\eta+\frac{(\omega_{\nabla U}(r))^{2}}{N_{B}}\right)k\eta+r\omega_{\nabla U}(r)}\\ &\quad+\mathrm{e}^{Cd}\exp\left(-\frac{k\eta}{Cc_{\text{P}}(\pi)}\right). \end{align*}

We give a version of the error estimate for convenience; to see that the convergence $\omega_{\nabla U}(r)\downarrow 0$ is unnecessary, we consider a rough version of the upper bound by replacing $\omega_{\nabla U}(r)$ with the constant $\omega_{\nabla U}(1)$ .

Corollary 3.3. Under (C1)–(C3) and (A6), there exists a constant $C\ge1$ independent of $N_{B}$ , r, k, $\eta$ , d, and $c_{\text{P}}(\pi)$ such that for all $N_{B}\in\mathbb{N}$ , $k\in\mathbb{N}$ , $\eta\in(0,1\wedge (m/(4(\omega_{\nabla U}(1))^{2}))]$ , and $r\in(0,1]$ with $\left(d^{2}r^{-1}\eta+N_{B}^{-1}\right)k\eta+r\le 1$ ,

\begin{align*} \mathcal{W}_{2}\left(\mu_{k\eta},\pi\right)&\le C\sqrt{d}\sqrt[4]{\left(d^{2}r^{-1}\eta+N_{B}^{-1}\right)k\eta+r}+\mathrm{e}^{Cd}\exp\left(-\frac{k\eta}{Cc_{\text{P}}(\pi)}\right).\end{align*}

We obtain the following estimate of the sampling complexity; the proof is identical to that of Proposition 3.1.

Proposition 3.2. Assume (C1)–(C3) and (A6) and fix $\epsilon\in(0,1]$ . If $r=\epsilon^{4}/48C^{4}d^{2}$ , $N_{B}\ge 48C^{4}d^{2}(Cc_{\text{P}}(\pi)(\log(2/\epsilon)+Cd)+1)/\epsilon^{4}$ , and $\eta$ satisfies

\begin{align*} \eta\le 1&\wedge \frac{m}{4\left(\omega_{\nabla U}(1)\right)^{2}}\wedge \frac{r\epsilon^{4}}{48C^{4}d^{4}(Cc_{\text{P}}(\pi)(\log(2/\epsilon)+Cd)+1)}, \end{align*}

then $\mathcal{W}_{2}\left(\mu_{k\eta},\pi\right)\le \epsilon$ for $k=\lceil Cc_{\text{P}}(\pi)(\log(2/\epsilon)+Cd)/\eta\rceil$ .

Since the complexities of $N_{B}$ and k are given as $N_{B}=\mathcal{O}(d^{2}c_{\text{P}}(\pi)(\log\epsilon^{-1}+d)/\epsilon^{4})$ and $k=\mathcal{O}(d^{6}c_{\text{P}}(\pi)^{2}(\log\epsilon^{-1}+d)^{2}/\epsilon^{8})$ , we obtain the sampling complexity of SS-LMC as $N_{B}k=\mathcal{O}(d^{8}c_{\text{P}}(\pi)^{3}(\log\epsilon^{-1}+d)^{3}/\epsilon^{12})$ or $N_{B}k=\widetilde{\mathcal{O}}(d^{11}c_{\text{P}}(\pi)^{3}/\epsilon^{12})$ , where $\widetilde{\mathcal{O}}$ ignores logarithmic factors.

3.3. The spherically smoothed stochastic gradient Langevin Monte Carlo algorithm

We consider a sampling algorithm for potentials such that for some $N\in\mathbb{N}$ ,

(5) \begin{align} U\left(x\right)=\frac{1}{N}\sum_{\ell=1}^{N}U_{\ell}\left(x\right),\end{align}

where $U_{\ell}(x)$ are non-negative functions with the following assumptions.

1. (D1) For all $\ell=1,\ldots,N$ , $U_{\ell}\in W_{\text{loc}}^{1,\infty}(\mathbb{R}^{d})$ .

2. (D2) $|\nabla U_{\ell}(\mathbf{0})|<\infty$ for all $\ell=1,\ldots,N$ and there exists a function $\hat{\omega} \, : \, [0,\infty)\to[0,\infty)$ such that for all $r\in(0,1]$ and $\ell=1,\ldots,N$ ,

   \begin{align*} \sup_{x,y\in\mathbb{R}^{d}:|x-y|\le r}\left|\nabla U_{\ell}(x)-\nabla U_{\ell}(y)\right|\le \hat{\omega}\left(r\right)<\infty. \end{align*}

3. (D3) There exist $m,b>0$ such that for all $x\in\mathbb{R}^{d}$ ,

   \begin{align*} \left\langle x,\nabla U\left(x\right)\right\rangle &\ge m\left|x\right|^{2}-b. \end{align*}

We define the stochastic gradient

\begin{align*} {G}\left(x,a_{i\eta}\right)=\frac{1}{N_{B}}\sum_{j=1}^{N_{B}}\nabla U_{\lambda_{i,j}}\left(x+r'\zeta_{i,j}\right),\end{align*}

where $N_{B}\in\mathbb{N}$ , $r'\in(0,1]$ , $a_{i\eta}=[\lambda_{i,1},\ldots,\lambda_{i,N_{B}},\zeta_{i,1},\ldots,\zeta_{i,N_{B}}]$ , $\{\lambda_{i,j}\}$ is a sequence of i.i.d. random variables with the discrete uniform distribution on the integers $1,\ldots,N$ , and $\{\zeta_{i,j}\}$ is a sequence of i.i.d. random variables with the density $\rho$ and independence of $\{\lambda_{i,j}\}$ . Then for any $x\in\mathbb{R}^{d}$ , we have

$$E[{G}(x,a_{i\eta})]=\nabla \bar{U}_{r'}(x)$$

and

\begin{align*} E\left[\left|{G}\left(x,a_{i\eta}\right)-\nabla \bar{U}_{r'}(x)\right|^{2}\right]&= \frac{1}{N_{B}}E\left[\left|\nabla U_{\lambda_{i,j}}\left(x+r'\zeta_{i,j}\right)-\nabla \bar{U}_{r'}(x)\right|^{2}\right]\\ &\le \frac{1}{N_{B}}E\left[\left|\nabla U_{\lambda_{i,j}}\left(x+r'\zeta_{i,j}\right)\right|^{2}\right]\\ &\le \frac{2}{N_{B}}\max_{\ell=1,\ldots,N}((\omega_{\nabla U_{\ell}}(1))^{2}|x|^{2}+(|\nabla U_{\ell}(\mathbf{0})|+2\omega_{\nabla U_{\ell}}(1))^{2})\end{align*}

by Lemma 4.4. We obtain (A5) with $\delta_{\mathbf{b},r',0}=\delta_{\mathbf{b},r',2}=0$ , $\delta_{\mathbf{v},0}=(\max_{\ell}|\nabla U_{\ell}(\mathbf{0})|+2\hat{\omega}(1))^{2})/N_{B}$ , and $\delta_{\mathbf{v},2}=(\hat{\omega}(1))^{2}/N_{B}$ . We call this algorithm the spherically smoothed stochastic gradient Langevin Monte Carlo algorithm.

Assumptions (D1)–(D3) yield (A1)–(A5) with the same discussion as for SS-LMC.

Corollary 3.4 (Error estimate of SS-SG-LMC.) Under (D1)–(D3) and (A6), there exists a constant $C\ge 1$ independent of $N_{B},r,k,\eta,d,c_{\text{P}}(\pi)$ such that for all $k\in\mathbb{N}$ , $\eta\in(0,1\wedge (m/(8(\hat{\omega}(1))^{2}))]$ , $r\in(0,1]$ , and $N_{B}\in\mathbb{N}$ with $(d^{2}(\hat{\omega}(r)/r)\eta+d/N_{B})k\eta+r\hat{\omega}(r)\le 1$ ,

\begin{align*} \mathcal{W}_{2}\left(\mu_{k\eta},\pi\right)\le C\sqrt{d}\sqrt[4]{\left(d^{2}\frac{\hat{\omega}(r)}{r}\eta+\frac{d}{N_{B}}\right)k\eta+r\hat{\omega}(r)}+\mathrm{e}^{Cd}\exp\left(-\frac{k\eta}{Cc_{\text{P}}(\pi)}\right). \end{align*}

We give a rough upper bound by replacing $\hat{\omega}(r)$ with the constant $\hat{\omega}(1)$ as in the discussion on SS-LMC.

Corollary 3.5. Under (D1)–(D3) and (A6), there exists a constant $C\ge1$ independent of $N_{B}$ , r, k, $\eta$ , d, and $c_{\text{P}}(\pi)$ such that for all $N_{B}\in\mathbb{N}$ , $k\in\mathbb{N}$ , $\eta\in(0,1\wedge (m/(8(\hat{\omega}(1))^{2}))]$ , and $r\in(0,1]$ with $\left(d^{2}r^{-1}\eta+dN_{B}^{-1}\right)k\eta+r\le 1$ ,

\begin{align*} \mathcal{W}_{2}\left(\mu_{k\eta},\pi\right)&\le C\sqrt{d}\sqrt[4]{\left(d^{2}r^{-1}\eta+dN_{B}^{-1}\right)k\eta+r}+\mathrm{e}^{Cd}\exp\left(-\frac{k\eta}{Cc_{\text{P}}(\pi)}\right).\end{align*}

Using this estimate, we yield the following estimate of the sampling complexity, which is lower than that of SS-LMC for U given by Eq. (5) if $N>d$ since the complexity to compute G in SS-LMC for this U increases by a factor of N and the sampling complexity of SS-SG-LMC deteriorates by a factor of d in comparison to that of SS-LMC.

Proposition 3.3. Assume (D1)–(D3) and (A6) and fix $\epsilon\in(0,1]$ . If $r=\epsilon^{4}/48C^{4}d^{2}$ , $N_{B}\ge 48C^{4}d^{3}(Cc_{\text{P}}(\pi)(\log(2/\epsilon)+Cd)+1)/\epsilon^{4}$ , and $\eta$ satisfies

\begin{align*} \eta\le 1&\wedge \frac{m}{8\left(\hat{\omega}(1)\right)^{2}}\wedge \frac{r\epsilon^{4}}{48C^{4}d^{4}(Cc_{\text{P}}(\pi)(\log(2/\epsilon)+Cd)+1)}, \end{align*}

then $\mathcal{W}_{2}\left(\mu_{k\eta},\pi\right)\le \epsilon$ for $k=\lceil Cc_{\text{P}}(\pi)(\log(2/\epsilon)+Cd)/\eta\rceil$ .

3.4. Zeroth-order Langevin algorithms

Let us consider a zeroth-order version of SS-LMC as an analogue to [Reference Roy, Shen, Balasubramanian and Ghadimi32] with the following G under (C1)–(C3) and the assumption $|U(x)|<\infty$ for all $x\in\mathbb{R}^{d}$ :

\begin{align*} G(x,a_{i\eta})=\frac{1}{N_{B}}\sum_{j=1}^{N_{B}}G_{j}(x,a_{i\eta})\,:\!=\,\frac{1}{N_{B}}\sum_{j=1}^{N_{B}}\frac{U\left(x+r'\zeta_{i,j}\right)-U\left(x\right)}{r'}\frac{4\zeta_{i,j}}{\left(1-|\zeta_{i,j}|^{2}\right)},\end{align*}

where $N_{B}\in\mathbb{N}$ , $r'\in(0,1]$ , and $\{\zeta_{i,j}\}$ is an i.i.d. sequence of random variables with the density $\rho$ . The fact that

\begin{align*} \frac{U\left(x+r'\zeta_{i,j}\right)-U\left(x\right)}{r'}\frac{4\zeta_{i,j}}{\left(1-|\zeta_{i,j}|^{2}\right)}=\frac{U\left(x+r'\zeta_{i,j}\right)-U\left(x\right)}{r'}\frac{-\nabla \rho\left(\zeta_{i,j}\right)}{\rho\left(\zeta_{i,j}\right)},\end{align*}

the symmetricity of $\rho$ , approximation of $\rho\in \mathcal{C}_{0}^{1}(\mathbb{R}^{d})\cap W^{1,1}(\mathbb{R}^{d})$ , and the essential boundedness of U and $\nabla U$ on compact sets by Lemmas 4.4 and 4.6 yield that for all $x\in\mathbb{R}^{d}$ ,

\begin{align*} E\left[G_{j}(x,a_{i\eta})\right] &=\int_{\mathbb{R}^{d}}\frac{U\left(x+r'z\right)-U\left(x\right)}{r'}\frac{-\nabla \rho\left(z\right)}{\rho\left(z\right)}\rho\left(z\right)\text{d} z\\ &=-\int_{\mathbb{R}^{d}}\frac{U\left(x+r'z\right)-U\left(x\right)}{r'}\nabla \rho\left(z\right)\text{d} z\\ &=-\int_{\mathbb{R}^{d}}\left(U\left(x+y\right)-U\left(x\right)\right)\left(\frac{1}{(r')^{d+1}}\nabla \rho\left(\frac{y}{r'}\right)\right)\text{d} y\\ &=\int_{\mathbb{R}^{d}}\nabla U\left(x+y\right)\rho_{r'}\left(y\right)\text{d} y\\ &=\nabla \bar{U}_{r'}\left(x\right).\end{align*}

Lemma 4.5, the convexity of $f(a)=a^{2}$ with $a\in\mathbb{R}$ , and the equality

\begin{align*} \int_{B_{1}(\mathbf{0})}\frac{\left|\nabla \rho\left(z\right)\right|^{2}}{\rho\left(z\right)}\text{d} z &=\frac{16\Gamma(d/2)}{\pi^{d/2}\text{B}(d/2,3)}\int_{B_{1}(\mathbf{0})}\left|z\right|^{2}\text{d} z =\frac{32}{\text{B}(d/2,3)}\int_{0}^{1}r^{d+1}\text{d} r\\ &=\frac{32\Gamma(d/2+3)}{\Gamma(d/2)\Gamma(3)(d+2)} =\frac{16(d/2+2)(d/2+1)(d/2)}{(d+2)}\\ &=2d(d+4)\end{align*}

give that for almost all $x\in\mathbb{R}^{d}$ ,

\begin{align*} E\left[\left|G_{1}(x,a_{i\eta})\right|^{2}\right] &=\int_{B_{1}(\mathbf{0})}\left(\frac{U\left(x+r'z\right)-U\left(x\right)}{r'}\right)^{2}\frac{\left|\nabla \rho\left(z\right)\right|^{2}}{\rho\left(z\right)}\text{d} z\\ &\le \left(\frac{3}{2}\left\|\nabla U\right\|_{\mathbb{M}}+\omega_{\nabla U}\left(1\right)\left|x\right|\right)^{2}\int_{B_{1}(\mathbf{0})}\frac{\left|\nabla \rho\left(z\right)\right|^{2}}{\rho\left(z\right)}\text{d} z\\ &= 2d(d+4)\left(\frac{3}{2}\left\|\nabla U\right\|_{\mathbb{M}}+\omega_{\nabla U}\left(1\right)\left|x\right|\right)^{2}\\ &\le d(d+4)\left(9\left\|\nabla U\right\|_{\mathbb{M}}^{2}+4\left(\omega_{\nabla U}(1)\right)^{2}\left|x\right|^{2}\right).\end{align*}

These properties along with

\begin{align*} E\left[\left|G(x,a_{i\eta})-\nabla \bar{U}_{r}(x)\right|^{2}\right]=\frac{1}{N_{B}}E\left[\left|G_{1}(x,a_{i\eta})-\nabla \bar{U}_{r}(x)\right|^{2}\right]\le \frac{1}{N_{B}}E\left[\left|G_{1}(x,a_{i\eta})\right|^{2}\right]\end{align*}

yield (A5) with $\delta_{\mathbf{b},r,0}=\delta_{\mathbf{b},r,2}=0$ , $\delta_{\mathbf{v},0}=9d(d+4)\left\|\nabla U\right\|_{\mathbb{M}}^{2}/2N_{B}$ , and $\delta_{\mathbf{v},2}=2d(d+4)(\omega_{\nabla U}(1))^{2}/N_{B}$ if $r=r'$ . Hence, the SG-LMC with this G also can achieve $\mathcal{W}_{2}(\mu_{k\eta},\pi)\le \epsilon$ for arbitrary $\epsilon>0$ . Note that the complexity deteriorates by a factor of $\mathcal{O}(d^{3})$ in comparison to SS-LMC; the batch size $N_{B}$ to achieve $\mathcal{W}_{2}(\mu_{k\eta},\pi)\le \epsilon$ is of order $\mathcal{O}(d^{5}c_{\text{P}}(\pi)(\log\epsilon^{-1}+d)/\epsilon^{4})$ since $\delta_{\mathbf{v},2}=0$ does not hold and both $\delta_{\mathbf{v},0}$ and $\delta_{\mathbf{v},2}$ are of order $\mathcal{O}(d^{2}/N_{B})$ .

We can also consider a zeroth-order version of SS-SG-LMC with the potential U in Eq. (5) and the following G under (D1)–(D3) and the assumption $|U_{\ell}(x)|<\infty$ for all $\ell=1,\ldots,N$ and $x\in\mathbb{R}^{d}$ :

\begin{align*} G(x,a_{i\eta})=\frac{1}{N_{B}}\sum_{j=1}^{N_{B}}G_{j}(x,a_{i\eta})\,:\!=\,\frac{1}{N_{B}}\sum_{j=1}^{N_{B}}\frac{U_{\lambda_{i,j}}\left(x+r'\zeta_{i,j}\right)-U_{\lambda_{i,j}}\left(x\right)}{r'}\frac{4\zeta_{i,j}}{1-|\zeta_{i,j}|^{2}},\end{align*}

where $N_{B}\in\mathbb{N}$ , $r'\in(0,1]$ , $a_{i\eta}=[\lambda_{i,1},\ldots,\lambda_{i,N_{B}},\zeta_{i,1},\ldots,\zeta_{i,N_{B}}]$ , $\{\lambda_{i,j}\}$ is a sequence of i.i.d. random variables with the discrete uniform distribution on $\{1,\ldots,N\}$ , and $\{\zeta_{i,j}\}$ is a sequence of i.i.d. random variables with the density $\rho$ and independence of $\{\lambda_{i,j}\}$ . We see that for all $x\in\mathbb{R}^{d}$ ,

\begin{align*} E\left[G(x,a_{i\eta})\right]=\frac{1}{NN_{B}}\sum_{j=1}^{N_{B}}\sum_{\ell=1}^{N}\int\frac{U_{\ell}\left(x+r'z\right)-U_{\ell}\left(x\right)}{r'}(\! - \! \nabla \rho(z))\text{d} z =\nabla \bar{U}_{r'}(x),\end{align*}

and for almost all $x\in\mathbb{R}^{d}$ ,

\begin{align*} E\left[\left|G(x,a_{i\eta})-\nabla \bar{U}_{r'}(x)\right|^{2}\right] &\le \frac{1}{N_{B}}E\left[\left|\frac{U_{\lambda_{i,j}}\left(x+r'\zeta_{i,j}\right)-U_{\lambda_{i,j}}\left(x\right)}{r'}\frac{4\zeta_{i,j}}{(1-|\zeta_{i,j}|^{2})}\right|^{2}\right]\\ &=\frac{1}{NN_{B}}\sum_{\ell=1}^{N}\int\left|\frac{U_{\ell}\left(x+r'z\right)-U_{\ell}\left(x\right)}{r'}\right|^{2}\frac{\left|\nabla \rho(z)\right|^{2}}{\rho(z)}\text{d} z\\ &\le \frac{1}{N_{B}}d(d+4)\max_{\ell=1,\ldots,N}\left(9\left\|\nabla U_{\ell}\right\|_{\mathbb{M}}^{2}+4\left(\hat{\omega}(1)\right)^{2}\left|x\right|^{2}\right).\end{align*}

Hence, assumption (A5) for this G holds with $\delta_{\mathbf{b},r',0}=\delta_{\mathbf{b},r',2}=0$ , $\delta_{\mathbf{v},0}=9d(d+4)(\max_{\ell}\left|\nabla U_{\ell}(\mathbf{0})\right|+\hat{\omega}(1))^{2}/2N_{B}$ , and $\delta_{\mathbf{v},2}=2d(d+4)(\hat{\omega}(1))^{2}/N_{B}$ if $r=r'$ . Therefore, this SG-LMC can achieve $\mathcal{W}_{2}(\mu_{k\eta},\pi)\le \epsilon$ for any $\epsilon>0$ , though the complexity is worse than that of SS-SG-LMC by a factor of $\mathcal{O}(d^{2})$ .

4.1. The fundamental theorem of calculus for weakly differentiable functions

We use the following result on the fundamental theorem of calculus for functions in $W_{\text{loc}}^{1,\infty}(\mathbb{R}^{d})$ .

Proposition 4.1. (Lieb and Loss [Reference Lieb and Loss23], Anastassiou [Reference Anastassiou2].) For each $f\in W_{\text{loc}}^{1,\infty}(\mathbb{R}^{d})$ , for almost all $x,y\in\mathbb{R}^{d}$ ,

(6) \begin{align} f(y)-f(x)=\int_{0}^{1}\left\langle \nabla f\left(x+t\left(y-x\right)\right),y-x\right\rangle \text{d} t. \end{align}

4.2. Properties of the compact polynomial mollifier

We analyse the mollifier $\rho$ proposed in Eq. (4). Note that our non-asymptotic analysis needs mollifiers of class $\mathcal{C}^{1}$ whose gradients have explicit $L^{1}$ -bounds and whose supports are in the unit ball of $\mathbb{R}^{d}$ , and it is non-trivial to obtain explicit $L^{1}$ -bounds for the gradients of well-known $\mathcal{C}^{\infty}$ mollifiers.

The following lemma gives some properties of $\rho$ .

We show the optimality of the compact polynomial mollifier; the $L^{1}$ -norms of the gradients of $\mathcal{C}^{1}$ non-negative mollifiers with supports in $B_{1}(\mathbf{0})$ are bounded below by d.

Lemma 4.2. Assume that $p \, : \, \mathbb{R}^{d}\to[0,\infty)$ is a continuously differentiable non-negative function whose support is in the unit ball of $\mathbb{R}^{d}$ such that $\int p(x)\text{d} x=1$ . Then

\begin{align*} \int_{\mathbb{R}^{d}}\left|\nabla p(x)\right|\text{d} x\ge d.\end{align*}

Proof. Since $p\in \mathcal{C}^{1}(\mathbb{R}^{d})$ , the $L^{1}$ -norm of the gradient equals the total variation; that is, for arbitrary $R> 1$ ,

\begin{align*} \int_{\mathbb{R}^{d}}\left|\nabla p(x)\right|\text{d} x&=\int_{B_{R}\left(\mathbf{0}\right)}\left|\nabla p(x)\right|\text{d} x\\ &=\sup\left\{\int_{B_{R}\left(\mathbf{0}\right)}p(x)\mathrm{div}\varphi(x)\text{d} x\left|\varphi\in\mathcal{C}_{0}^{1}\left(B_{R}\left(\mathbf{0}\right) \, ; \, \mathbb{R}^{d}\right),\left\|\varphi\right\|_{\infty}\le 1\right.\right\}, \end{align*}

where $\mathcal{C}_{0}^{1}(B_{R}(\mathbf{0});\mathbb{R}^{d})$ is a class of continuously differentiable functions $\varphi \, : \, \mathbb{R}^{d}\to\mathbb{R}^{d}$ with compact supports in $B_{R}(\mathbf{0})\subset\mathbb{R}^{d}$ . For all $\delta\in(0,1]$ , by fixing $\varphi_{\delta}\in\mathcal{C}_{0}^{1}(B_{R}(\mathbf{0});\mathbb{R}^{d})$ such that $\varphi_{\delta}(x)=(1-\delta)x$ for all $x\in B_{1}(\mathbf{0})$ and $\|\varphi_{\delta}\|_{\infty}\le 1$ , we have

\begin{align*} \int_{\mathbb{R}^{d}}\left|\nabla p(x)\right|\text{d} x&\ge \int_{B_{R}\left(\mathbf{0}\right)}p(x)\mathrm{div}\varphi_{\delta}(x)\text{d} x=\int_{B_{1}\left(\mathbf{0}\right)}p(x)\mathrm{div}\varphi_{\delta}(x)\text{d} x=(1-\delta)d. \end{align*}

We obtain the conclusion by taking the limit as $\delta\to0$ .

4.3. Functions with the finite moduli of continuity and their properties

We consider a class of possibly discontinuous functions and show lemmas useful for the analysis of SG-LMC such that $\nabla U$ and $\widetilde{G}$ are in this class.

Let $\mathbb{M}=\mathbb{M}(\mathbb{R}^{d};\mathbb{R}^{\ell})$ with fixed $d,\ell\in\mathbb{N}$ denote a class of measurable functions $\phi \, : \, (\mathbb{R}^{d},\mathcal{B}(\mathbb{R}^{d}))\to(\mathbb{R}^{\ell},\mathcal{B}(\mathbb{R}^{\ell}))$ with (1) $|\phi(\mathbf{0})|<\infty$ and (2) $\omega_{\phi}(1)<\infty$ , where $\omega_{\phi}(\! \cdot \!)$ is the well-known modulus of continuity defined as

\begin{align*} \omega_{\phi}(r)\,:\!=\,\sup\nolimits_{x,y\in\mathbb{R}^{d}:|x-y|\le r}\left|\phi(x)-\phi(y)\right|,\end{align*}

where $r>0$ . Note that we use the modulus of continuity not to measure the continuity of $\phi$ , but to measure the fluctuation of $\phi$ within $\bar{B}_{r}(x)$ for all $x\in\mathbb{R}^{d}$ . An intuitive element of $\mathbb{M}$ is $\mathbb{I}_{A}$ for an arbitrary measurable set $A\in\mathcal{B}(\mathbb{R}^{d})$ because $\omega_{\mathbb{I}_{A}}(r)\le 1$ for any A and $r>0$ . In the rest of the paper, we sometimes use the notation $\|\phi\|_{\mathbb{M}}\,:\!=\,|\phi(\mathbf{0})|+\omega_{\phi}(1)$ with $\phi\in\mathbb{M}$ just for brevity (it is easy to see that $\mathbb{M}$ equipped with $\|\cdot\|_{\mathbb{M}}$ is a Banach space).

We introduce the following lemma; this ensures that we can change $r>0$ arbitrarily if $\omega_{\phi}(r)<\infty$ with some $r>0$ , and reveal that considering $r=1$ is sufficient to capture the large-scale behaviour since the lemma leads to $\omega_{\phi}(n)\le n\omega_{\phi}(1)$ for any $n\in\mathbb{N}$ .

Proof. $\omega_{\phi}(r)\le \sup_{t>0}\lceil t\rceil^{-1}\omega_{\phi}(rt)$ and $\omega_{\phi}(r)\ge \lceil t\rceil^{-1}\omega_{\phi}(rt)$ with $t\in(0,1]$ hold immediately. Thus, we only examine $\omega_{\phi}(r)\ge \lceil t\rceil^{-1}\omega_{\phi}(rt)$ for all $t>1$ .

We fix $t>1$ . For any $x,y\in \mathbb{R}^{d}$ with $|x-y|\le rt$ ,

\begin{align*} \left|\phi\left(x\right)-\phi\left(y\right)\right|&\le \sum_{i=1}^{\lceil t\rceil}\left|\phi\left(\frac{(\lceil t\rceil-i+1)x+(i-1)y}{\lceil t\rceil}\right)-\phi\left(\frac{(\lceil t\rceil-i)x+iy}{\lceil t\rceil}\right)\right|\\ &\le \lceil t\rceil \omega_{\phi}(r) \end{align*}

because $|((\lceil t\rceil-i+1)x+(i-1)y)/\lceil t\rceil-((\lceil t\rceil-i)x+iy)/\lceil t\rceil|=|x-y|/\lceil t\rceil\le r$ .

Moreover, continuity along with the boundedness of the modulus of continuity does not imply uniform continuity, which we can easily observe by $f(x)=\sin(x^{2})$ with $x\in\mathbb{R}$ .

Figure 1. The chains of implications.

Lemma 4.4. (Linear growth of functions with the finite moduli of continuity.) For any $\phi\in\mathbb{M}(\mathbb{R}^{d};\mathbb{R}^{\ell})$ , we have that for all $x\in\mathbb{R}^{d}$ ,

\begin{align*} \left|\phi\left(x\right)\right| \le \left|\phi(\mathbf{0})\right|+\omega_{\phi}(1)+\omega_{\phi}(1)|x|. \end{align*}

Proof. Fix $x\in\mathbb{R}^{d}$ . Lemma 4.3 gives

\begin{align*} \left|\phi\left(x\right)\right|-\left|\phi(\mathbf{0})\right|\le \left|\phi\left(x\right)-\phi(\mathbf{0})\right|\le \omega_{\phi}(|x|)\le \lceil |x|\rceil\omega_{\phi}(1)\le (1+|x|)\omega_{\phi}(1). \end{align*}

Therefore, the statement holds.

Lemma 4.5 (Local Lipschitz continuity by gradients with the finite moduli of continuity.) Assume that $\Phi\in W_{\text{loc}}^{1,\infty}(\mathbb{R}^{d})$ and a representative weak gradient $\nabla \Phi$ is in $\mathbb{M}(\mathbb{R}^{d};\mathbb{R}^{d})$ . Then for almost all $x,y\in\mathbb{R}^{d}$ ,

\begin{align*} \left|\Phi\left(x\right)-\Phi\left(y\right)\right|\le \left(\left|\nabla\Phi(\mathbf{0})\right|+\omega_{\nabla\Phi}(1)\left(1+\frac{\left|x\right|+\left|y\right|}{2}\right)\right)\left|y-x\right|. \end{align*}

Proof. Proposition 4.1 and Lemma 4.4 yield that for almost all $x,y\in\mathbb{R}^{d}$ ,

\begin{align*} |\Phi(x)-\Phi(y)|&= \left|\int_{0}^{1}\left\langle\nabla \Phi\left(x+t(y-x)\right),y-x\right\rangle\text{d} t\right|\\ &\le \int_{0}^{1}\left|\nabla \Phi\left(x+t(y-x)\right)\right|\text{d} t\left|y-x\right|\\ &\le \int_{0}^{1}\left(\left|\nabla \Phi\left(\mathbf{0}\right)\right|+\omega_{\nabla \Phi}\left(1\right)\left(1+\left|(1-t)x+ty\right|\right)\right)\text{d} t\left|y-x\right|\\ &\le \left(\left|\nabla \Phi\left(\mathbf{0}\right)\right|+\omega_{\nabla\Phi}(1)\left(1+\frac{|x|+|y|}{2}\right)\right)\left|y-x\right|.\end{align*}

Hence, the lemma is proved.

Lemma 4.6. (Quadratic growth by gradients with the finite moduli of continuity.) Assume that $\Phi\in W_{\text{loc}}^{1,\infty}(\mathbb{R}^{d})$ and a representative weak gradient $\nabla \Phi$ is in $\mathbb{M}(\mathbb{R}^{d};\mathbb{R}^{d})$ . Then we have that $\|\Phi\|_{L^{\infty}(B_{1}(\mathbf{0}))}<\infty$ and for almost all $x\in\mathbb{R}^{d}$ ,

\begin{align*} \Phi\left(x\right)&\le \frac{\omega_{\nabla \Phi}(1)}{2}\left|x\right|^{2}+\left(\left|\nabla \Phi\left(\mathbf{0}\right)\right|+\frac{3}{2}\omega_{\nabla \Phi}(1)\right)\left|x\right|+\left\|\Phi\right\|_{L^{\infty}\left(B_{1}\left(\mathbf{0}\right)\right)}. \end{align*}

Moreover, for all $x\in\mathbb{R}^{d}$ and $r\in(0,1]$ ,

\begin{align*} \bar{\Phi}_{r}(x)&\le \frac{\omega_{\nabla \Phi}(1)}{2}\left|x\right|^{2}+\left(\left|\nabla \Phi\left(\mathbf{0}\right)\right|+2\omega_{\nabla \Phi}(1)\right)\left|x\right|+\left\|\Phi\right\|_{L^{\infty}\left(B_{1}\left(\mathbf{0}\right)\right)} \end{align*}

with $\bar{\Phi}_{r}(x)=(\Phi\ast\rho_{r})(x)$ .

Proof. Lemma 4.5 gives that for almost all $x\in\mathbb{R}^{d}$ and $y \in B_{1}(\mathbf{0})\cap B_{|x|}(x)$ ,

\begin{align*} \Phi\left(x\right)&\le \frac{\omega_{\nabla \Phi}(1)}{2}\left|x\right|\left|x-y\right|+\left(\left|\nabla \Phi\left(\mathbf{0}\right)\right|+\frac{3}{2}\omega_{\nabla \Phi}(1)\right)\left|x-y\right|+\Phi\left(y\right)\\ &\le \frac{\omega_{\nabla \Phi}(1)}{2}\left|x\right|^{2}+\left(\left|\nabla \Phi\left(\mathbf{0}\right)\right|+\frac{3}{2}\omega_{\nabla \Phi}(1)\right)\left|x\right|+\left\|\Phi\right\|_{L^{\infty}\left(B_{1}\left(\mathbf{0}\right)\right)}. \end{align*}

Regarding the second statement, we have that

\begin{align*} \bar{\Phi}_{r}(x)&=\int_{\mathbb{R}^{d}} \Phi\left(x-y\right)\rho_{r}\left(y\right)\text{d} y\\ &=\int_{\mathbb{R}^{d}} \left(\Phi\left(-y\right)+\int_{0}^{1}\left\langle\nabla \Phi\left(-y+tx\right),x\right\rangle \text{d} t\right)\rho_{r}\left(y\right)\text{d} y\\ &\le \int_{\mathbb{R}^{d}} \left(\Phi\left(-y\right)+\int_{0}^{1}\left(\left|\nabla \Phi(\mathbf{0})\right|+\omega_{\nabla \Phi}\left(1\right)\left(1+\left|-y+tx\right|\right)\right)\left|x\right|\text{d} t\right)\rho_{r}\left(y\right)\text{d} y\\ &\le \left\|\Phi\right\|_{L^{\infty}(B_{1}(\mathbf{0}))}+\int_{0}^{1}\left(\left|\nabla \Phi(\mathbf{0})\right|+\omega_{\nabla \Phi}\left(1\right)\left(2+t\left|x\right|\right)\right)\left|x\right|\text{d} t\\ &\le \frac{\omega_{\nabla \Phi}(1)}{2}\left|x\right|^{2}+\left(\left|\nabla \Phi\left(\mathbf{0}\right)\right|+2\omega_{\nabla \Phi}(1)\right)\left|x\right|+\left\|\Phi\right\|_{L^{\infty}(B_{1}(\mathbf{0}))}. \end{align*}

The lemma is proved.

Lemma 4.8 (Bounded gradients of convolution.) For all $\phi\in\mathbb{M}(\mathbb{R}^{d};\mathbb{R}^{\ell})$ , $r>0$ , and $x\in\mathbb{R}^{d}$ , we have that

\begin{align*} \left\|\nabla \bar{\phi}_{r}(x)\right\|_{2}\le \left(d+2\right)\frac{\omega_{\phi}(r)}{r},\end{align*}

where $\bar{\phi}_{r}(x)=(\phi\ast\rho_{r})(x)$ .

Proof. We obtain

\begin{align*} \nabla \left(\bar{\phi}_{r}\right)(x) &=\int_{\mathbb{R}^{d}}\phi\left(y\right)\left(\nabla \rho_{r}\right)\left(x-y\right)\text{d} y =\int_{\mathbb{R}^{d}}\left(\phi\left(y\right)-\phi\left(x\right)\right)\left(\nabla \rho_{r}\right)\left(x-y\right)\text{d} y\end{align*}

by using $\int\nabla \rho_{r}(x)\text{d} x=0$ , and thus

\begin{align*} \left\|\nabla \left(\bar{\phi}_{r}\right)(x)\right\|_{2}&\le \int_{\mathbb{R}^{d}}\left\|\left(\phi\left(y\right)-\phi\left(x\right)\right)\left(\nabla \rho_{r}\right)\left(x-y\right)\right\|_{2}\text{d} y\\ &= \int_{\mathbb{R}^{d}}\left|\phi\left(y\right)-\phi\left(x\right)\right|\left|\nabla \rho_{r}\left(x-y\right)\right|\text{d} y\le \omega_{\phi}(r) \int_{\mathbb{R}^{d}}\left|\nabla \rho_{r}\left(y\right)\right|\text{d} y\\ &= \omega_{\phi}(r)\int_{\mathbb{R}^{d}}\left| \frac{1}{r^{d+1}}\nabla\rho\left(\frac{y}{r}\right)\right|\text{d} y = \omega_{\phi}(r)\int_{\mathbb{R}^{d}}\left|\frac{1}{r^{d+1}}\nabla \rho\left(z\right)\right|r^{d}\text{d} z\\ &\le \frac{(d+2)\omega_{\phi}(r)}{r}\end{align*}

by the change of variables $z=y/r$ with $r^{d}\text{d} z=\text{d} y$ and Lemma 4.1.

Lemma 4.9. (1-Lipschitz mapping to $\ell^{\infty}$ .) For all $\phi\in\mathbb{M}(\mathbb{R}^{d};\mathbb{R}^{\ell})$ and $r>0$ ,

\begin{align*} \left\|\phi\ast \rho_{r}-\phi\right\|_{\infty}\le \omega_{\phi}(r). \end{align*}

Proof. Since $\int\rho_{r}(x)\text{d} x=1$ , for all $x\in\mathbb{R}^{d}$ ,

\begin{align*} \left|\phi\ast\rho_{r}(x)-\phi(x)\right|&=\left|\int_{\mathbb{R}^{d}}\phi(y)\rho_{r}(x-y)\text{d} y-\phi(x)\right|\\ &=\left|\int_{\mathbb{R}^{d}}\phi(y)\rho_{r}(x-y)\text{d} y-\int_{\mathbb{R}^{d}}\phi(x)\rho_{r}(x-y)\text{d} y\right|\\ &=\left|\int_{\mathbb{R}^{d}}\left(\phi(y)-\phi(x)\right)\rho_{r}(x-y)\text{d} y\right|\\ &\le \int_{\mathbb{R}^{d}}\left|\phi(y)-\phi(x)\right|\rho_{r}(x-y)\text{d} y\\ &\le \omega_{\phi}(r). \end{align*}

This is the desired conclusion.

Lemma 4.10 (Essential supremum of deviations by convolution). Assume that $\Phi\in W_{\text{loc}}^{1,\infty}(\mathbb{R}^{d})$ and a representative weak gradient $\nabla \Phi$ is in $\mathbb{M}(\mathbb{R}^{d};\mathbb{R}^{d})$ . For all $r>0$ ,

\begin{align*} \left\|\bar{\Phi}_{r}-\Phi\right\|_{L^{\infty}(\mathbb{R}^{d})}\le r\omega_{\nabla \Phi}(r) \end{align*}

with $\bar{\Phi}_{r}(x)\,:\!=\,(\Phi\ast\rho_{r})(x)$ .

Proof. By Proposition 4.1 and $\int_{\mathbb{R}^{d}}\langle y,z\rangle \rho_{r}(y)\text{d} y=0$ for any $z\in\mathbb{R}^{d}$ , for almost all $x\in\mathbb{R}^{d}$ ,

\begin{align*} \left|\bar{\Phi}_{r}(x)-\Phi(x)\right|&=\left|\int_{\mathbb{R}^{d}}\left(\Phi(x-y)-\Phi(x)\right)\rho_{r}(y)\text{d} y\right|\\ &=\left|\int_{\mathbb{R}^{d}}\left(\int_{0}^{1}\left\langle \nabla \Phi(x-ty),y\right\rangle\text{d} t\right)\rho_{r}(y)\text{d} y\right|\\ &=\left|\int_{\mathbb{R}^{d}}\left(\int_{0}^{1}\left\langle \nabla \Phi(x-ty)-\nabla \Phi(x),y\right\rangle\text{d} t\right)\rho_{r}(y)\text{d} y\right|\\ &\le \omega_{\nabla \Phi}(r)\int_{\mathbb{R}^{d}}|y|\rho_{r}(y)\text{d} y\\ &\le r\omega_{\nabla \Phi}(r) \end{align*}

and thus the statement holds.

4.4. Liptser–Shiryaev-type condition for change of measures

We show the existence of explicit likelihood ratios of diffusion-type processes based on Theorem 7.19 and Lemma 7.6 of [Reference Liptser and Shiryaev24]. We fix $T>0$ throughout this section. Let $(W_{T},\mathcal{W}_{T})$ be a measurable space of $\mathbb{R}^{d}$ -valued continuous functions $w_{t}$ with $t\in[0,T]$ and $\mathcal{W}_{T}=\sigma(w_{s} \, : \, w\in W_{T},s\le T)$ . We also use the notation $\mathcal{W}_{t}=\sigma(w_{s} \, : \, w\in W_{T}, s\le t)$ for $t\in[0,T]$ . Let $(\Omega, \mathcal{F}, \mu)$ be a complete probability space and $(\tilde{\Omega},\tilde{\mathcal{F}},\tilde{\mu})$ be an identical copy of it. We assume that the filtration $\{\mathcal{F}_{t}\}_{t\in[0,T]}$ satisfies the usual conditions. Let $(B_{t},\mathcal{F}_{t})$ with $t\in[0,T]$ be a d-dimensional Brownian motion and $\xi$ be an $\mathcal{F}_{0}$ -measurable d-dimensional random vector such that $|\xi|<\infty $ $\mu$ -almost surely. We set $\{a_{t}\}_{t\in[0,T]}$ , an $\mathcal{F}_{t}$ -adapted random process such that its trajectory $\{a_{s}(\omega)\}_{s\in[0,t]}$ with $\omega\in\Omega$ for each $t\in[0,T]$ is in a measurable space $(A_{t},\mathcal{A}_{t})$ . Assume that $a=\{a_{t}\}_{t\in[0,T]}$ , $B=\{B_{t}\}_{t\in[0,T]}$ , and $\xi$ are independent of each other. $\mu_{a},\mu_{B}$ , and $\mu_{\xi}$ denote the probability measures induced by a, B, and $\xi$ on $(A_{T},\mathcal{A}_{T})$ , $(W_{T},\mathcal{W}_{T})$ , and $(\mathbb{R}^{d},\mathcal{B}(\mathbb{R}^{d}))$ , respectively.

Consider the solutions $X^{P}=\{X_{t}^{P}\}_{t\in[0,T]}$ and $X^{Q}=\{X_{t}^{Q}\}_{t\in[0,T]}$ of the following SDEs:

(7) \begin{align} \text{d} X_{t}^{P}&=b^{P}\left(t,a,X^{P}\right)\text{d} t+\sqrt{2\beta^{-1}}\text{d} B_{t},\ X_{0}^{P}=\xi, \end{align}

(8) \begin{align} \text{d} X_{t}^{Q}&=b^{Q}\left(X_{t}^{Q}\right)\text{d} t+\sqrt{2\beta^{-1}}\text{d} B_{t},\ X_{0}^{Q}=\xi.\end{align}

We set the following assumptions, partially adapted from [Reference Liptser and Shiryaev24] but containing some differences in $\xi$ and the structure of $X^{Q}$ .

1. (LS1) $X_{t}^{P}$ is a strong solution of Eq. (7), that is, there exists a measurable functional $F_{t}$ for each t such that

   \begin{align*} X_{t}^{P}(\omega)=F_{t}(a(\omega),B(\omega),\xi(\omega)) \end{align*}
   $\mu$ -almost surely.

2. (LS2) $b^{P}$ is non-anticipative, that is, $\mathcal{A}_{t}\times \mathcal{W}_{t}$ -measurable for each $t\in[0,T]$ , and for fixed $a\in A_{T}$ and $w\in W_{T}$ ,

   \begin{align*} \int_{0}^{T}\left|b^{P}(t,a,w)\right|\text{d} t<\infty. \end{align*}

3. (LS3) $b^{Q} \, : \, \mathbb{R}^{d}\to\mathbb{R}^{d}$ is Lipschitz continuous, so that $X^{Q}$ is the unique strong solution of Eq. (8).

4. (LS4) We have that

   \begin{align*} &\mu\left(\int_{0}^{T}\left(\left|b^{P}\left(t,a,X^{P}\right)\right|^{2}+\left|b^{Q}\left(X_{t}^{P}\right)\right|^{2}\right)\text{d} t<\infty\right)\\ &\quad=\mu\left(\int_{0}^{T}\left(\left|b^{P}\left(t,a,X^{Q}\right)\right|^{2}+\left|b^{Q}\left(X_{t}^{Q}\right)\right|^{2}\right)\text{d} t<\infty\right)=1. \end{align*}

We consider a variant of (7) with fixed $a\in A_{T}$ :

\begin{align*} \text{d} X_{t}^{P|a}=b^{P}\left(t,a,X^{P|a}\right)\text{d} t+\sqrt{2\beta^{-1}}\text{d} B_{t},\ X_{0}^{P|a}=\xi.\end{align*}

Then assumption (LS1) yields that

(9) \begin{align} X_{t}^{P|a}(\omega)=F_{t}(a,B(\omega),\xi(\omega))\end{align}

$\mu_{a}\times \mu$ -almost surely. We assume that $\Omega=A_{T}\times W_{T}\times \mathbb{R}^{d}$ , $\mathcal{F}=\mathcal{A}_{T}\times \mathcal{W}_{T}\times\mathcal{B}(\mathbb{R}^{d})$ , and $\mu=\mu_{a}\times\mu_{B}\times\mu_{\xi}$ without loss of generality. Then each $\omega\in\Omega$ has the form $\omega=(a,B,\xi)$ and we can assume that a, B, and $\xi$ are projections such as $a(\omega)=a$ , $B(\omega)=B$ , and $\xi(\omega)=\xi$ ; therefore, Eq. (9) holds $\mu_{a}\times\mu_{B}\times\mu_{\xi}$ -almost surely.

We consider a process on the product space $(\Omega\times\tilde{\Omega},\mathcal{F}\times\tilde{\mathcal{F}},\mu\times \tilde{\mu})$ :

\begin{align*} \text{d} X_{t}^{P|a(\omega)}(\tilde{\omega})=b^{P}\left(t,a(\omega),X^{P|a(\omega)}(\tilde{\omega})\right)\text{d} t+\sqrt{2\beta^{-1}}\text{d} B_{t}(\tilde{\omega}),\ X_{0}^{P|a(\omega)}=\xi(\tilde{\omega}).\end{align*}

Assumption (LS1) gives that

\begin{align*} X_{t}^{P|a(\omega)}(\tilde{\omega})=F_{t}\left(a(\omega),B(\tilde{\omega}),\xi(\tilde{\omega})\right)\end{align*}

$\mu\times\tilde{\mu}$ -almost surely.

Lemma 4.11. Under (LS1), for any $C\in\mathcal{W}_{T}$ ,

\begin{align*} \mu\left(X^{P}(a,B,\xi)\in C|\sigma(a)\right)=\tilde{\mu}\left(X^{P}\left(a,\tilde{B},\tilde{\xi}\right)\left(\tilde{\omega}\right)\in C\right) \end{align*}

$\mu$ -almost surely.

Proof. The proof is essentially identical to that of Lemma 7.5 of [Reference Liptser and Shiryaev24] except for the randomness of $\xi$ . We first show that for fixed $t\in[0,T]$ and $C_{t}\in\mathcal{B}(\mathbb{R}^{d})$ ,

\begin{align*} \mu\left(F_{t}(a,B,\xi)\in C_{t}|\sigma(a)\right)=\tilde{\mu}\left(F_{t}\left(a,\tilde{B},\tilde{\xi}\right)\in C_{t}\right) \end{align*}

$\mu$ -almost surely. Note that the following probability for fixed a is $\mathcal{A}_{T}$ -measurable owing to (LS1) and Fubini’s theorem:

\begin{align*} \tilde{\mu}\left(F_{t}\left(a,\tilde{B},\tilde{\xi}\right)\in C_{t}\right)=\left(\mu_{B}\times\mu_{\xi}\right)\left(F_{t}(a,B,\xi)\in C_{t}\right). \end{align*}

Let $f(a(\omega))$ be a $\sigma(a)$ -measurable bounded random variable. Again Fubini’s theorem gives that

\begin{align*} E\left[f(a(\omega))\mathbb{I}_{F_{t}(a,B,\xi)\in C_{t}}\right]&=\int_{A_{T}}\int_{W_{T}}\int_{\mathbb{R}^{d}}f(a)\mathbb{I}_{F_{t}(a,w,x)\in C_{t}}\mu_{a}(\text{d} a)\mu_{B}(\text{d} w)\mu_{\xi}(\text{d} x)\\ &=\int_{A_{T}}f(a)\left(\int_{W_{T}}\int_{\mathbb{R}^{d}}\mathbb{I}_{F_{t}(a,w,x)\in C_{t}}\mu_{B}(\text{d} w)\mu_{\xi}(\text{d} x)\right)\mu_{a}(\text{d} a)\\ &=\int_{A_{T}}f(a)\left(\mu_{B}\times \mu_{\xi}\right)\left(F_{t}(a,B,\xi)\in C_{t}\right)\mu_{a}(\text{d} a)\\ &=\int_{A_{T}}f(a)\tilde{\mu}\left(F_{t}\left(a,\tilde{B},\tilde{\xi}\right)\in C_{t}\right)\mu_{a}(\text{d} a)\\ &=E\left[f(a)\tilde{\mu}\left(F_{t}\left(a,\tilde{B},\tilde{\xi}\right)\in C_{t}\right)\right] \end{align*}

and thus the definition of conditional expectation yields the result. Similarly, we obtain that for all $n\in\mathbb{N}$ , $0\le t_{1} < \cdots < t_{n}\le T$ , and $C_{t_{i}}\in \mathcal{B}(\mathbb{R}^{d})$ , $i=1,\ldots,n$ ,

\begin{align*} &\mu\left(F_{t_{1}}(a,B,\xi)\in C_{t_{1}}\cdots F_{t_{n}}(a,B,\xi)\in C_{t_{n}}|\sigma(a)\right)\\ &\quad=\tilde{\mu}\left(F_{t_{1}}\left(a,\tilde{B},\tilde{\xi}\right)\in C_{t_{1}},\cdots,F_{t_{n}}\left(a,\tilde{B},\tilde{\xi}\right)\in C_{t_{n}}\right). \end{align*}

Therefore, the statement holds true.

Let $P_{T}$ and $Q_{T}$ denote the laws of $\{(a_{t},X_{t}^{P}) \, : \, t\in[0,T]\}$ and $\{(a_{t},X_{t}^{Q}) \, : \, t\in[0,T]\}$ . Note that $a_{t}$ and $X_{t}^{Q}$ are independent of each other by the assumptions. The following proposition gives the equivalence and the representation of the likelihood ratio.

Proposition 4.2. Under (LS1)–(LS4), we have that

\begin{align*} &\frac{\text{d} Q_{T}}{\text{d} P_{T}}\left(a,X^{P}\right)\\ &\quad=\exp\left(-\sqrt{\frac{\beta}{2}}\int_{0}^{T}\left\langle \left(b^{P}-b^{Q}\right)\left(t,a,X^{P}\right),\text{d} B_{t}\right\rangle -\frac{\beta}{4}\int_{0}^{T}\left|\left(b^{P}-b^{Q}\right)\left(t,a,X^{P}\right)\right|^{2}\text{d} t\right). \end{align*}

Proof. The proof is quite parallel to that of Lemma 7.6 of [Reference Liptser and Shiryaev24]. For arbitrary set $\Gamma=\Gamma_{1}\times \Gamma_{2}$ , $\Gamma_{1}\in\mathcal{A}_{T}$ and $\Gamma_{2}\in\mathcal{W}_{T}$ , by Lemma 4.11,

\begin{align*} \mu\left(\left(a,X^{P}\right)\in\Gamma\right)&=\int_{A_{T}\times W_{T}\times \mathbb{R}^{d}}\mathbb{I}_{a\in \Gamma_{1}}\mathbb{I}_{X^{P}(a,w,x)\in\Gamma_{2}}\mu_{a}(\text{d} a)\mu_{B}\left(\text{d} w\right)\mu_{\xi}\left(\text{d} x\right)\\ &=\int_{a\in \Gamma_{1}}\mu\left(X^{P}(a,B,\xi)\in \Gamma_{2}|\sigma(a)\right)\mu_{a}(\text{d} a)\\ &=\int_{a\in \Gamma_{1}}\tilde{\mu}\left(X^{P}\left(a,\tilde{B},\tilde{\xi}\right)\in \Gamma_{2}\right)\mu_{a}(\text{d} a)\\ &=\int_{a\in \Gamma_{1}}\left(P|a\right)_{T}(\Gamma_{2})\mu_{a}(\text{d} a),\end{align*}

where $(P|a)_{T}$ is the law of (9). Let $(Q|a)_{T}$ denote the law of $X^{Q}$ . For $\mu_{a}$ -almost all a, under (LS1)–(LS4) and Theorem 7.19 of [Reference Liptser and Shiryaev24], $(P|a)_{T}\sim (Q|a)_{T}$ and the likelihood ratio is given as

\begin{align*} &\frac{\text{d} (P|a)_{T}}{\text{d} (Q|a)_{T}}\left(X^{Q}\right)\\ &\quad=\exp\left(\frac{\beta}{2}\int_{0}^{T}\left\langle \left(b^{P}-b^{Q}\right)\left(t,a,X^{Q}\right),\text{d} B_{t}\right\rangle -\frac{\beta^{2}}{4}\int_{0}^{T}\left|\left(b^{P}-b^{Q}\right)\left(t,a,X^{Q}\right)\right|^{2}\text{d} t\right).\end{align*}

Therefore, we have

\begin{align*} \mu\left(\left(a,X^{P}\right)\in\Gamma\right)&=\int_{\Gamma_{1}}\int_{\Gamma_{2}}\left(\frac{\text{d} (P|a)_{T}}{\text{d} (Q|a)_{T}}(\text{d} w)(Q|a)_{T}(\text{d} w)\right)\mu_{a}(\text{d} a)\\ &=\int_{\Gamma_{1}}\int_{\Gamma_{2}}\frac{\text{d} (P|a)_{T}}{\text{d} (Q|a)_{T}}(\text{d} w)\left(\mu_{a}\times (Q|a)_{T}\right)(\text{d} a \text{d} w)\\ &=\int_{\Gamma}\frac{\text{d} (P|a)_{T}}{\text{d} (Q|a)_{T}}(\text{d} w)Q_{T}(\text{d} a \text{d} w).\end{align*}

Since $Q_{T}(a,w \, : \, (\text{d} (P|a)_{T})/(\text{d} (Q|a)_{T})(w)=0)=0$ , Lemma 6.8 of [Reference Liptser and Shiryaev24] yields the desired conclusion.

We obtain the following result.

Proposition 4.3 (Kullback–Leibler divergence.) Under (LS1)–(LS4) and the assumption

\begin{equation*} E\left[\int_{0}^{T}\left|\left(b^{P}-b^{Q}\right)\left(s,a,X^{P}\right)\right|^{2}\text{d} s\right]<\infty, \end{equation*}

we obtain

\begin{align*} D\left(P_{T}\left\|Q_{T}\right.\right)= \frac{\beta}{4}E\left[\int_{0}^{T}\left|\left(b^{P}-b^{Q}\right)\left(s,a,X^{P}\right)\right|^{2}\text{d} s\right]. \end{align*}

Proof. Using Proposition 4.2, we obtain

\begin{align*} &D\left(P_{T}\left\|Q_{T}\right.\right)\\ &\quad=E\left[\log\left(\frac{\text{d} P_{T}}{\text{d} Q_{T}}\right)(a,X^{P})\right]\\ &\quad=E\left[\frac{\beta}{4}\int_{0}^{T}\left|\left(b^{P}-b^{Q}\right)\left(s,a,X^{P}\right)\right|^{2}\text{d} s+\sqrt{\frac{\beta}{2}}\int_{0}^{T}\left\langle \left(b^{P}-b^{Q}\right)\left(s,a,X^{P}\right),\text{d} B_{s}\right\rangle\right]\\ &\quad=\frac{\beta}{4}E\left[\int_{0}^{T}\left|\left(b^{P}-b^{Q}\right)\left(s,a,X^{P}\right)\right|^{2}\text{d} s\right], \end{align*}

since the local martingale term is a martingale by the assumption. Hence, the proposition holds.

4.5. Poincaré inequalities

Let us consider Poincaré inequalities for a probability measure $P_{\Phi}$ whose density is $\left(\int \mathrm{e}^{-\Phi(x)}\text{d} x\right)^{-1}\mathrm{e}^{-\Phi(x)}$ with lower-bounded $\Phi\in\mathcal{C}^{2}(\mathbb{R}^{d})$ such that $\int \mathrm{e}^{-\Phi(x)}\text{d} x<\infty$ . Let $L\,:\!=\,\Delta -\langle \nabla \Phi,\nabla \rangle$ , which is $P_{\Phi}$ -symmetric, $P_{t}$ be the Markov semigroup with the infinitesimal generator L, and $\mathcal{E}$ denote the Dirichlet form

\begin{align*} \mathcal{E}(g)\,:\!=\,\lim_{t\to0}\frac{1}{t}\int_{\mathbb{R}^{d}}g \left(g-P_{t}g\right) \text{d} P_{\Phi},\end{align*}

where $g\in L^{2}(P_{\Phi})$ such that the limit exists. Here, we say that a probability measure $P_{\Phi}$ satisfies a Poincaré inequality with constant $c_\textrm{P}(P_{\Phi})$ (the Poincaré constant) if for any $Q\ll P_{\Phi}$ ,

\begin{align*} \chi^{2}\left(Q\|P_{\Phi}\right)\le c_\textrm{P}(P_{\Phi})\mathcal{E}\left(\sqrt{\frac{\text{d} Q}{\text{d} P_{\Phi}}}\right).\end{align*}

We adopt the following statement from [Reference Raginsky, Rakhlin and Telgarsky31]; although it is different from the original discussion of [Reference Bakry, Barthe, Cattiaux and Guillin4], the difference is negligible because Eq. (2.3) of [Reference Bakry, Barthe, Cattiaux and Guillin4] yields the same upper bound.

Proposition 4.4. (Bakry et al. [Reference Bakry, Barthe, Cattiaux and Guillin4].) Assume that there exists a Lyapunov function $V\in\mathcal{C}^{2}(\mathbb{R}^{d})$ with $V:\mathbb{R}^{d}\to[1,\infty)$ such that

\begin{align*} \frac{LV\left(x\right)}{V\left(x\right)}\le -\lambda_{0}+\kappa_{0}\mathbb{I}_{B_{\tilde{R}}(\mathbf{0})}\left(x\right) \end{align*}

for some $\lambda_{0}>0$ , $\kappa_{0}\ge 0$ , and $\tilde{R}>0$ , where $LV(x)=\Delta V-\langle \nabla \Phi,\nabla V\rangle$ . Then $P_{\Phi}$ satisfies a Poincaré inequality with constant $c_\textrm{P}(P_{\Phi})$ such that

\begin{align*} c_\textrm{P}(P_{\Phi})\le \frac{1}{\lambda_{0}}\left(1+a\kappa_{0}\tilde{R}^{2}\mathrm{e}^{\mathrm{Osc}_{\tilde{R}}}\right), \end{align*}

where $a>0$ is an absolute constant and $\mathrm{Osc}_{\tilde{R}}\,:\!=\,\max_{x:|x|\le \tilde{R}}\Phi(x)-\min_{x:|x|\le \tilde{R}}\Phi(x)$ .

The next proposition shows the convergence in $\mathcal{W}_{2}$ by $\chi^{2}$ -divergence using the recent study by [Reference Liu25].

Proposition 4.5. (Lehec [Reference Lehec21], Lemma 9.) Assume that $P_{\Phi}$ satisfies Poincaré inequalities with constant $c_\textrm{P}(P_{\Phi})$ and $\nabla \Phi$ is at most of linear growth. Then for any probability measure $\nu$ on $(\mathbb{R}^{d},\mathcal{B}(\mathbb{R}^{d}))$ with $\nu\ll P_{\Phi}$ and every $t>0$ ,

\begin{align*} \mathcal{W}_{2}\left(\nu P_{t},P_{\Phi}\right)\le \sqrt{2c_\textrm{ P}(P_{\Phi})\chi^{2}\left(\nu\|P_{\Phi}\right)}\exp\left(-\frac{t}{2c_\textrm{P}(P_{\Phi})}\right), \end{align*}

where $\nu P_{t}$ is the law of the unique weak solution $Z_{t}$ of the SDE

\begin{align*} \text{d} Z_{t}=-\nabla \Phi\left(Z_{t}\right)\text{d} t+\sqrt{2}\text{d} B_{t},\quad Z_{0}\sim \nu. \end{align*}

4.6. A bound for the 2-Wasserstein distance by KL divergence

The next proposition is an immediate result by [Reference Bolley and Villani7].

Proposition 4.6. (Bolley and Villani [Reference Bolley and Villani7].) Let $\mu,\nu$ be probability measures on $(\mathbb{R}^{d},\mathcal{B}(\mathbb{R}^{d}))$ . Assume that there exists a constant $\lambda>0$ such that $\int \exp(\lambda |x|^{2})\nu(\text{d} x)<\infty$ . Then for any $\mu$ ,

\begin{align*} \mathcal{W}_{2}\left(\mu,\nu\right)\le C_{\nu}\left(D(\mu\|\nu)^{1/2}+\left(\frac{D(\mu\|\nu)}{2}\right)^{1/4}\right), \end{align*}

where

\begin{align*} C_{\nu}\,:\!=\,2\inf_{\lambda>0}\left(\frac{1}{\lambda}\left(\frac{3}{2}+\log\int_{\mathbb{R}^{d}}\mathrm{e}^{\lambda\left|x\right|^{2}}\nu\left(\text{d} x\right)\right)\right)^{1/2}. \end{align*}

5.1. Moments of SG-LMC algorithms

1. (1) For all $k\in\mathbb{N}$ and $0<\eta\le 1\wedge (\tilde{m}/2((\omega_{\widetilde{G}}(1))^{2}+\delta_{\mathbf{v},2}))$ , $Y_{k\eta},G(Y_{k\eta},a_{k\eta})\in L^{2}$ . Moreover,

   \begin{align*} \sup_{k\ge 0}E\left[\left|Y_{k\eta}\right|^{2}\right]&\le \kappa_{0}+2\left(1\vee \frac{1}{\tilde{m}}\right)\left(\tilde{b}+\left\|\widetilde{G}\right\|_{\mathbb{M}}^{2}+\delta_{\mathbf{v},0}+\frac{d}{\beta}\right)= \! : \, \kappa_{\infty}. \end{align*}

2. (2) For any $t\ge0$ and $r\in(0,1]$ ,

   \begin{align*} E\left[\left|\bar{X}_{t}^{r}\right|^{2}\right]&\le \kappa_{0}\mathrm{e}^{-2\bar{m}t}+\frac{\bar{b}+d/\beta}{\bar{m}}\left(1-\mathrm{e}^{-2\bar{m}t}\right). \end{align*}

Proof. The proof is adapted from Lemma 3 of [Reference Raginsky, Rakhlin and Telgarsky31].

1. (1) We first show $Y_{k\eta}\in L^{2}$ for each $k\in\mathbb{N}$ since

   \begin{align*} E\left[\left.\left|G(Y_{k\eta},a_{k\eta})\right|^{2}\right|Y_{k\eta}\right]&\le 2E\left[\left.\left|G(Y_{k\eta},a_{k\eta})-\widetilde{G}(Y_{k\eta})\right|^{2}\right|Y_{k\eta}\right]+2\left|\widetilde{G}(Y_{k\eta})\right|^{2}\\ &\le 4\delta_{\mathbf{v},2}\left|Y_{k\eta}\right|^{2}+4\delta_{\mathbf{v},0}+\left(4\left\|\widetilde{G}\right\|_{\mathbb{M}}+4\left(\omega_{\widetilde{G}}\left(1\right)\right)^{2}\left|Y_{k\eta}\right|^{2}\right) \end{align*}
   almost surely and thus $Y_{k\eta}\in L^{2}$ implies $G(Y_{k\eta},a_{k\eta})\in L^{2}$ . Assumptions (A3), (A5), and (A6) together with Lemma 4.4 give
   \begin{align*} E\left[\left|Y_{(k+1)\eta}\right|^{2}\right] &=E\left[\left|Y_{k\eta}-\eta{G}\left(Y_{k\eta},a_{k\eta}\right)+\sqrt{2\beta^{-1}}\left(B_{(k+1)\eta}-B_{k\eta}\right)\right|^{2}\right]\\ &\le 2E\left[\left|Y_{k\eta}-\eta{G}\left(Y_{k\eta},a_{k\eta}\right)\right|^{2}\right]+2E\left[\left|\sqrt{2\beta^{-1}}\left(B_{(k+1)\eta}-B_{k\eta}\right)\right|^{2}\right]\\ &\le 4E\left[\left|Y_{k\eta}-\eta \widetilde{G}(Y_{k\eta})\right|^{2}\right]+4\eta^{2}E\left[\left|\widetilde{G}(Y_{k\eta})-{G}\left(Y_{k\eta},a_{k\eta}\right)\right|^{2}\right]+\frac{4\eta d}{\beta}\\ &\le \left(8+16(\omega_{\widetilde{G}}(1))^{2}+8\delta_{\mathbf{v},2}\right)E\left[\left|Y_{k\eta}\right|^{2}\right]+\left(16\left\|\widetilde{G}\right\|_{\mathbb{M}}^{2}+8\delta_{\mathbf{v},0}+\frac{4d}{\beta}\right). \end{align*}
   Hence, $Y_{k\eta}\in L^{2}$ as there exist $\gamma_{2},\gamma_{0}>1$ such that
   \begin{align*} E[|Y_{(k+1)\eta}|^{2}] &\le \gamma_{2}E[|Y_{k\eta}|^{2}]+\gamma_{0}\\ &\le \gamma_{2}^{k+1}E[|\xi|^{2}]+\gamma_{0}(\gamma_{2}^{k+1}-1)/(\gamma_{2}-1)\\ &\le \gamma_{2}^{k+1}(\log E[\exp(|\xi|^{2})]+\gamma_{0}/(\gamma_{2}-1))\\ &\le \gamma_{2}^{k+1}(\kappa_{0}+\gamma_{0}/(\gamma_{2}-1))<\infty \end{align*}
   for arbitrary $k\in\mathbb{N}$ by Jensen’s inequality.

The independence among $Y_{k\eta}$ , $a_{k\eta}$ , and $B_{(k+1)\eta}-B_{k\eta}$ and the square integrability of $Y_{k\eta}$ and $G(Y_{k\eta},a_{k\eta})$ lead to

\begin{align*} E\left[\left|Y_{(k+1)\eta}\right|^{2}\right] &=E\left[\left|Y_{k\eta}-\eta \widetilde{G}(Y_{k\eta})\right|^{2}\right]+\eta^{2}E\left[\left|\widetilde{G}(Y_{k\eta})-{G}\left(Y_{k\eta},a_{k\eta}\right)\right|^{2}\right]+\frac{2\eta d}{\beta}. \end{align*}

Lemma 4.4 gives

\begin{align*} &E\left[\left|Y_{k\eta}-\eta \widetilde{G}(Y_{k\eta})\right|^{2}\right]\\ &\quad=E\left[\left|Y_{k\eta}\right|^{2}\right]-2\eta E\left[\left\langle Y_{k\eta}, \widetilde{G}(Y_{k\eta})\right\rangle \right]+\eta^{2}E\left[\left|\widetilde{G}(Y_{k\eta})\right|^{2}\right]\\ &\quad\le E\left[\left|Y_{k\eta}\right|^{2}\right]+2\eta\left(\tilde{b}-\tilde{m}E\left[\left|Y_{k\eta}\right|^{2}\right]\right)+2\eta^{2}\left(\left\|\widetilde{G}\right\|_{\mathbb{M}}^{2}+\left(\omega_{\widetilde{G}}(1)\right)^{2}E\left[\left|Y_{k\eta}\right|^{2}\right]\right)\\ &\quad=\left(1-2\eta \tilde{m}+2\eta^{2}\left(\omega_{\widetilde{G}}(1)\right)^{2}\right)E\left[\left|Y_{k\eta}\right|^{2}\right]+2\eta \tilde{b}+2\eta^{2}\left\|\widetilde{G}\right\|_{\mathbb{M}}^{2}. \end{align*}

By assumption (A5) and the independence between $a_{k\eta}$ and $Y_{k\eta}$ , we also have

\begin{align*} E\left[\left|\widetilde{G}(Y_{k\eta})-{G}\left(Y_{k\eta},a_{k\eta}\right)\right|^{2}\right]\le 2\delta_{\mathbf{v},2}E\left[\left|Y_{k\eta}\right|^{2}\right]+2\delta_{\mathbf{v},0}. \end{align*}

Hence, for $\gamma\,:\!=\,1-2\eta \tilde{m}+2\eta^{2}((\omega_{\widetilde{G}}(1))^{2}+\delta_{\mathbf{v},2})<1$ , we have that

\begin{align*} E\left[\left|Y_{(k+1)\eta}\right|^{2}\right] &\le \gamma E\left[\left|Y_{k\eta}\right|^{2}\right]+2\eta \tilde{b}+2\eta^{2}\left\|\widetilde{G}\right\|_{\mathbb{M}}^{2}+2\eta^{2}\delta_{\mathbf{v},0}+\frac{2\eta d}{\beta}. \end{align*}

If $\gamma\le 0$ , then it is obvious that

\begin{align*} E\left[\left|Y_{(k+1)\eta}\right|^{2}\right]\le 2\tilde{b}+2\left\|\widetilde{G}\right\|_{\mathbb{M}}^{2}+2\delta_{\mathbf{v},0}+\frac{2d}{\beta}, \end{align*}

and if $\gamma\in(0,1)$ ,

\begin{align*} E\left[\left|Y_{k\eta}\right|^{2}\right]&\le \gamma^{k}E\left[\left|Y_{0}\right|^{2}\right]+\frac{2\eta \tilde{b}+2\eta^{2}\left\|\widetilde{G}\right\|_{\mathbb{M}}^{2}+2\eta^{2}\delta_{\mathbf{v},0}+\frac{2\eta d}{\beta}}{2\eta \tilde{m}-2\eta^{2}\left(\left(\omega_{\widetilde{G}}(1)\right)^{2}+\delta_{\mathbf{v},2}\right)}\\ &\le E\left[\left|Y_{0}\right|^{2}\right]+\frac{2\tilde{b}+2\eta\left\|\widetilde{G}\right\|_{\mathbb{M}}^{2}+2\eta \delta_{\mathbf{v},0}+\frac{2d}{\beta}}{2\tilde{m}-2\eta\left(\left(\omega_{\widetilde{G}}(1)\right)^{2}+\delta_{\mathbf{v},2}\right)}\\ &\le \kappa_{0}+\frac{2}{\tilde{m}}\left(\tilde{b}+\left\|\widetilde{G}\right\|_{\mathbb{M}}^{2}+\delta_{\mathbf{v},0}+\frac{d}{\beta}\right) \end{align*}

since Jensen’s inequality yields $E[|\xi|^{2}]\le \log E[\exp(|\xi|^{2})]= \kappa_{0}$ .

(2) Itô’s formula yields

\begin{align*} \mathrm{e}^{2\bar{m}t}\left|\bar{X}_{t}^{r}\right|^{2}&=\left|\bar{X}_{0}^{r}\right|^{2}+\int_{0}^{t}\left(\mathrm{e}^{2\bar{m}s}\left\langle -\nabla U\left(\bar{X}_{s}^{r}\right),2\bar{X}_{s}^{r}\right\rangle+\mathrm{e}^{2\bar{m}s}\frac{2d}{\beta}+2\bar{m}\mathrm{e}^{2\bar{m}s}\left|\bar{X}_{s}^{r}\right|^{2}\right)\text{d} s\\ &\quad+\sqrt{2\beta^{-1}}\int_{0}^{t}\mathrm{e}^{2\bar{m}s}\left\langle 2\bar{X}_{s}^{r},\text{d} B_{s}\right\rangle. \end{align*}

The dissipativity and the martingale property of the last term lead to

\begin{align*} E\left[\left|\bar{X}_{t}^{r}\right|^{2}\right]&=\mathrm{e}^{-2\bar{m}t}E\left[\left|\xi\right|^{2}\right]\\ &\quad+2\int_{0}^{t}\mathrm{e}^{2\bar{m}(s-t)}\left(E\left[\left\langle -\nabla U\left(\bar{X}_{s}^{r}\right),\bar{X}_{s}^{r}\right\rangle+\bar{m}\left|\bar{X}_{s}^{r}\right|^{2}\right]+\frac{d}{\beta}\right)\text{d} s\\ &\le \mathrm{e}^{-2\bar{m}t}E\left[\left| \xi\right|^{2}\right]+2\int_{0}^{t}\mathrm{e}^{2\bar{m}(s-t)}\left(E\left[-\bar{m}\left|\bar{X}_{s}^{r}\right|^{2}+\bar{b}+\bar{m}\left|\bar{X}_{s}^{r}\right|^{2}\right]+\frac{d}{\beta}\right)\text{d} s\\ &\le \mathrm{e}^{-2\bar{m}t}\kappa_{0}+\frac{\bar{b}+d/\beta}{\bar{m}}\left(1-\mathrm{e}^{-2\bar{m}t}\right), \end{align*}

and we are done.

Lemma 5.2. (Exponential integrability of mollified Langevin dynamics.) Assume (A1)–(A4) and (A6). For all $r\in(0,1]$ and $\alpha\in(0,\beta \bar{m}/2)$ such that $E[\exp(\alpha|\xi|^{2})]<\infty$ ,

\begin{align*} E\left[\exp\left(\alpha |\bar{X}_{t}^{r}|^{2}\right)\right]&\le E\left[\mathrm{e}^{\alpha |\xi|^{2}}\right]\mathrm{e}^{-2\alpha \left(\bar{b}+d/\beta\right)t}+2 \mathrm{e}^{\frac{2\alpha(\bar{b}+d/\beta)}{\bar{m}-2\alpha/\beta}}\left(1- \mathrm{e}^{-2\alpha \left(\bar{b}+d/\beta\right)t}\right). \end{align*}

In particular, for $\alpha=1\wedge (\beta \bar{m}/4)$ ,

\begin{align*} \sup_{t\ge0}\log E\left[\mathrm{e}^{\alpha|\bar{X}_{t}^{r}|^{2}}\right]&\le \log\left(\mathrm{e}^{\alpha\kappa_{0}}\vee 2\mathrm{e}^{4\alpha(\bar{b}+d/\beta)/\bar{m}}\right)\le \alpha\kappa_{0}+\frac{4\alpha(\bar{b}+d/\beta)}{\bar{m}}+1. \end{align*}

Proof. Let $V_{\alpha}(x)\,:\!=\,\exp(\alpha|x|^{2})$ . Note that

\begin{align*} \nabla V_{\alpha}(x)= 2\alpha V_{\alpha}(x)x,\quad \nabla^{2} V_{\alpha}(x)=4\alpha^{2} V_{\alpha}(x)xx^{\top}+2\alpha V_{\alpha}(x)I_{d}. \end{align*}

Let $\bar{\mathcal{L}}^{r}$ denote the extended generator of $\bar{X}_{t}^{r}$ such that $\bar{\mathcal{L}}^{r}f\,:\!=\,\beta^{-1}\Delta f-\langle \nabla \bar{U}_{r}, \nabla f\rangle$ for $f\in\mathcal{C}^{2}(\mathbb{R}^{d})$ . We have that

\begin{align*} \bar{\mathcal{L}}^{r}V_{\alpha}(x)&\le -2\alpha V_{\alpha}(x)\left\langle \nabla \bar{U}_{r}(x),x\right\rangle +2(\alpha/\beta)V_{\alpha}(x)\left(2\alpha\left|x\right|^{2}+d\right)\\ &\le 2\alpha V_{\alpha}(x)\left(\left(-\bar{m}\left|x\right|^{2}+\bar{b}\right) +\left(2\alpha\left|x\right|^{2}+d\right)/\beta\right)\\ &=2\alpha V_{\alpha}(x)\left(\left(2\alpha/\beta-\bar{m}\right)\left|x\right|^{2}+\bar{b}+ d/\beta\right). \end{align*}

Let $R^{2}= 2(\bar{b}+d/\beta)/(\bar{m}-2\alpha/\beta)$ be a fixed constant. We obtain that for all $x\in\mathbb{R}^{d}$ with $|x|\ge R$ ,

\begin{align*} \bar{\mathcal{L}}^{r}V_{\alpha}(x)&\le -2\alpha \left(\bar{b}+ d/\beta\right)V_{\alpha}(x), \end{align*}

and trivially for all $x\in\mathbb{R}^{d}$ with $|x|<R$ ,

\begin{align*} \bar{\mathcal{L}}^{r}V_{\alpha}(x)&\le 2\alpha \mathrm{e}^{2\alpha(\bar{b}+d/\beta)/(\bar{m}-2\alpha/\beta)}\left(\bar{b}+d/\beta\right)\\ &\le 4\alpha \mathrm{e}^{2\alpha(\bar{b}+d/\beta)/(\bar{m}-2\alpha/\beta)}\left(\bar{b}+d/\beta\right)-2\alpha \left(\bar{b}+ d/\beta\right)V_{\alpha}(x). \end{align*}

Thus, we have for all $x\in\mathbb{R}^{d}$ ,

\begin{align*} \bar{\mathcal{L}}^{r}V_{\alpha}(x)\le -2\alpha \left(\bar{b}+ d/\beta\right)V_{\alpha}(x)+4\alpha \mathrm{e}^{2\alpha(\bar{b}+d/\beta)/(\bar{m}-2\alpha/\beta)}\left(\bar{b}+d/\beta\right). \end{align*}

By Itô’s formula, there exists a sequence of stopping times $\{\sigma_{n}\in[0,\infty)\}_{n\in\mathbb{N}}$ with $\sigma_{n}<\sigma_{n+1}$ for all $n\in\mathbb{N}$ and $\sigma_{n}\uparrow \infty$ as $n\to\infty$ almost surely such that for all $n\in\mathbb{N}$ and $t\ge0$ ,

\begin{align*} &E\left[\mathrm{e}^{2\alpha \left(\bar{b}+ d/\beta\right)(t\wedge\sigma_{n})}V_{\alpha}\left(\bar{X}_{t\wedge\sigma_{n}}^{r}\right)\right]\\ &\quad= E\left[V_{\alpha}\left(\bar{X}_{0}^{r}\right)\right]\\ &\qquad+E\left[\int_{0}^{t\wedge\sigma_{n}}\left(\mathrm{e}^{2\alpha \left(\bar{b}+ d/\beta\right)s}\bar{\mathcal{L}}^{r}V_{\alpha}\left(\bar{X}_{s}^{r}\right)+2\alpha \left(\bar{b}+ d/\beta\right)\mathrm{e}^{2\alpha \left(\bar{b}+ d/\beta\right)s}V_{\alpha}\left(\bar{X}_{s}^{r}\right)\right)\text{d} s\right]. \end{align*}

We have that

\begin{align*} &E\left[\int_{0}^{t\wedge\sigma_{n}}\left(\mathrm{e}^{2\alpha \left(\bar{b}+ d/\beta\right)s}\bar{\mathcal{L}}^{r}V_{\alpha}\left(\bar{X}_{s}^{r}\right)+2\alpha \left(\bar{b}+ d/\beta\right)\mathrm{e}^{2\alpha \left(\bar{b}+ d/\beta\right)s}V_{\alpha}\left(\bar{X}_{s}^{r}\right)\right)\text{d} s\right]\\ &\quad\le 4\alpha \mathrm{e}^{2\alpha(\bar{b}+d/\beta)/(\bar{m}-2\alpha/\beta)}\left(\bar{b}+d/\beta\right)E\left[\int_{0}^{t\wedge\sigma_{n}}\mathrm{e}^{2\alpha \left(\bar{b}+ d/\beta\right)s}\text{d} s\right]\\ &\quad\le 4\alpha \mathrm{e}^{2\alpha(\bar{b}+d/\beta)/(\bar{m}-2\alpha/\beta)}\left(\bar{b}+d/\beta\right)\int_{0}^{t}\mathrm{e}^{2\alpha \left(\bar{b}+ d/\beta\right)s}\text{d} s, \end{align*}

and thus Fatou’s lemma gives

\begin{align*} &E\left[\mathrm{e}^{2\alpha \left(\bar{b}+ d/\beta\right)t}V_{\alpha}\left(\bar{X}_{t}^{r}\right)\right]\\ &\quad=E\left[\lim_{n\to\infty} \mathrm{e}^{2\alpha \left(\bar{b}+ d/\beta\right)(t\wedge\sigma_{n})}V_{\alpha}\left(\bar{X}_{t\wedge\sigma_{n}}^{r}\right)\right]\\ &\quad\le \liminf_{n\to\infty}E\left[\mathrm{e}^{2\alpha \left(\bar{b}+ d/\beta\right)(t\wedge\sigma_{n})}V_{\alpha}\left(\bar{X}_{t\wedge\sigma_{n}}^{r}\right)\right]\\ &\quad\le E\left[V_{\alpha}\left(\bar{X}_{0}^{r}\right)\right]+4\alpha \mathrm{e}^{2\alpha(\bar{b}+d/\beta)/(\bar{m}-2\alpha/\beta)}\left(\bar{b}+d/\beta\right)\int_{0}^{t}\mathrm{e}^{2\alpha \left(\bar{b}+ d/\beta\right)s}\text{d} s. \end{align*}

Therefore,

\begin{align*} E\left[V_{\alpha}\left(\bar{X}_{t}^{r}\right)\right]&\le E\left[\mathrm{e}^{\alpha \left|\bar{X}_{0}^{r}\right|^{2}}\right]\mathrm{e}^{-2\alpha \left(\bar{b}+ d/\beta\right)t}+2 \mathrm{e}^{2\alpha(\bar{b}+d/\beta)/(\bar{m}-2\alpha/\beta)}\left(1- \mathrm{e}^{-2\alpha \left(\bar{b}+ d/\beta\right)t}\right) \end{align*}

and we obtain the desired conclusion.

5.2. Poincaré inequalities for distributions with mollified potentials

Let $\bar{L}^{r}$ be an operator such that $\bar{L}^{r}f\,:\!=\,\Delta f-\beta\langle \nabla \bar{U}_{r},\nabla f\rangle $ for all $f\in\mathcal{C}^{2}(\mathbb{R}^{d})$ . Note that Lemma 4.7 yields $\bar{U}_{r}\in\mathcal{C}^{2}(\mathbb{R}^{d})$ .

Lemma 5.3. (A bound for the constant of a Poincaràinequality for $\bar{\pi}^{r}$ .) Under (A1)–(A4), for some absolute constant $a>0$ , for all $r\in(0,1]$ ,

\begin{align*} c_\textrm{P}(\bar{\pi}^{r})&\le \frac{2}{\bar{m}\beta\left(d+\bar{b}\beta\right)}+\frac{4a\left(d+\bar{b}\beta\right)}{\bar{m}\beta}\exp\left(\beta\left(\frac{3}{2}\left\|\nabla U\right\|_{\mathbb{M}}\left(1+\frac{4\left(d+\bar{b}\beta\right)}{\bar{m}\beta}\right)+U_{0}\right)\right), \end{align*}

where $U_{0}\,:\!=\,\left\|U\right\|_{L^{\infty}(B_{1}(\mathbf{0}))}<\infty$ .

Proof. We adapt the discussion of [Reference Raginsky, Rakhlin and Telgarsky31]. We set a Lyapunov function $V(x)=\mathrm{e}^{\bar{m}\beta|x|^{2}/4}$ . Since $-\langle \nabla \bar{U}_{r}(x),x\rangle \le -\bar{m}|x|^{2}+\bar{b}$ for all $x\in\mathbb{R}^{d}$ , we have that

\begin{align*} \bar{L}^{r}V(x)&=\left(\frac{d\bar{m}\beta}{2}+\frac{\left(\bar{m}\beta\right)^{2}}{4}\left|x\right|^{2}-\frac{\bar{m}\beta^{2}}{2}\left\langle \nabla \bar{U}_{r}\left(x\right),x\right\rangle \right)V(x)\\ &\le \left(\frac{d\bar{m}\beta}{2}+\frac{\left(\bar{m}\beta\right)^{2}}{4}\left|x\right|^{2}-\frac{\bar{m}^{2}\beta^{2}}{2}\left|x\right|^{2}+\frac{\bar{m}\beta^{2}\bar{b}}{2}\right)V(x)\\ &= \left(\frac{\bar{m}\beta\left(d+\bar{b}\beta\right)}{2}-\frac{\bar{m}^{2}\beta^{2}}{4}|x|^{2}\right)V(x). \end{align*}

We fix the constants

\begin{align*} \kappa=\frac{\bar{m}\beta\left(d+\bar{b}\beta\right)}{2},\quad \gamma=\frac{\bar{m}^{2}\beta^{2}}{4},\quad \tilde{R}^{2}=\frac{2\kappa}{\gamma}=\frac{4\left(d+\bar{b}\beta\right)}{\bar{m}\beta}. \end{align*}

Lemma 4.6, $2a\le a^{2}+1$ for $a>0$ , and $U(x)\ge 0$ give $\left\|U\right\|_{L^{\infty}(B_{1}(\mathbf{0}))}<\infty$ and

\begin{align*} \text{Osc}_{\tilde{R}}\left(\beta \bar{U}_{r}\right)&\le \beta\left(\frac{\omega_{\nabla U}(1)}{2}\tilde{R}^{2}+2\left\|\nabla U\right\|_{\mathbb{M}}\tilde{R}+\left\|U\right\|_{L^{\infty}(B_{1}(\mathbf{0}))}\right)\\ &\le \beta\left(\left(\left\|\nabla U\right\|_{\mathbb{M}}+\frac{\omega_{\nabla U}(1)}{2}\right)\tilde{R}^{2}+\left\|\nabla U\right\|_{\mathbb{M}}+\left\|U\right\|_{L^{\infty}(B_{1}(\mathbf{0}))}\right)\\ &\le \beta\left(\frac{3}{2}\left\|\nabla U\right\|_{\mathbb{M}}\tilde{R}^{2}+\left\|\nabla U\right\|_{\mathbb{M}}+\left\|U\right\|_{L^{\infty}(B_{1}(\mathbf{0}))}\right)\\ &\le \beta\left(\frac{3}{2}\left\|\nabla U\right\|_{\mathbb{M}}\left(1+\tilde{R}^{2}\right)+\left\|U\right\|_{L^{\infty}(B_{1}(\mathbf{0}))}\right). \end{align*}

Proposition 4.4 with $\lambda_{0}=\kappa_{0}=\kappa$ yields that for some absolute constant $a>0$ ,

\begin{align*} c_\textrm{P}\left(\bar{\pi}^{r}\right)&\le \frac{2}{\bar{m}\beta\left(d+\bar{b}\beta\right)}\left(1+a\frac{\bar{m}\beta\left(d+\bar{b}\beta\right)}{2}\frac{4\left(d+\bar{b}\beta\right)}{\bar{m}\beta}\mathrm{e}^{\text{Osc}_{\tilde{R}}\left(\beta \bar{U}_{r}\right)}\right)\\ &=\frac{2}{\bar{m}\beta\left(d+\bar{b}\beta\right)}+\frac{4a\left(d+\bar{b}\beta\right)}{\bar{m}\beta}\exp\left(\beta\left(\frac{3}{2}\left\|\nabla U\right\|_{\mathbb{M}}\left(1+\frac{4\left(d+\bar{b}\beta\right)}{\bar{m}\beta}\right)+U_{0}\right)\right). \end{align*}

Hence, the statement holds true.

5.3. Kullback–Leibler and $\chi^{2}$ -divergences

Lemma 5.4. Under (A1)–(A6), for any $k\in\mathbb{N}$ and $\eta\in(0,1\wedge (\tilde{m}/2((\omega_{\widetilde{G}}(1))^{2}+\delta_{\mathbf{v},2}))]$ , we have that

\begin{align*} D(\mu_{k\eta}\|\bar{\nu}_{k\eta}^{r})\le \left(C_{0}\frac{\omega_{\nabla U}(r)}{r}\eta+\beta\left(\delta_{r,2}\kappa_{\infty}+\delta_{r,0}\right)\right)k\eta, \end{align*}

where $C_{0}$ is a positive constant such that

\begin{align*} C_{0}&=\left(d+2\right)\left(\frac{\beta}{3}\left(\left\|\widetilde{G}\right\|_{\mathbb{M}}^{2}+\delta_{\mathbf{v},0}+\left((\omega_{\widetilde{G}}(1))^{2}+\delta_{\mathbf{v},2}\right)\kappa_{\infty}\right)+\frac{d}{2}\right). \end{align*}

Proof. We set $A_{t}\,:\!=\,\{\mathbf{a}_{s}=a_{\lfloor s/\eta\rfloor \eta} \, : \, a_{i\eta}\in A,i=0,\ldots,\lfloor t/\eta\rfloor,s\le t\}$ with $t\le k\eta$ , and $\mathcal{A}_{t}\,:\!=\,\sigma(\{\mathbf{a}\in A_{t} \, : \, \mathbf{a}_{s_{j}}\in C_{j},j=1,\ldots,n\} \, : \, s_{j}\in[0,t],C_{j}\in\mathcal{A},n\in\mathbb{N})$ . Let $P_{k\eta}$ and $Q_{k\eta}$ denote the probability measures on $(A_{k\eta}\times W_{k\eta},\mathcal{A}_{k\eta}\times \mathcal{W}_{k\eta})$ of $\{(a_{\lfloor t/\eta\rfloor \eta},Y_{t}) \, : \, 0\le t\le T\}$ and $\{(a_{\lfloor t/\eta\rfloor \eta},\bar{X}_{t}^{r}) \, : \, 0\le t\le T\}$ , respectively. Note that $\bar{X}_{t}^{r}$ is the unique strong solution to Eq. (10) and such a unique strong solution of this equation exists for any $r>0$ since $\nabla \bar{U}_{r}$ is Lipschitz continuous by Lemma 4.8. We obtain

\begin{align*} &\frac{\beta}{4}E\left[\int_{0}^{k\eta}\left|\nabla \bar{U}_{r}\left(Y_{t}\right)-G\left(Y_{\lfloor t/\eta\rfloor \eta},a_{\lfloor t/\eta\rfloor\eta}\right)\right|^{2}\text{d} t\right]\\ &\quad\le \frac{\beta}{2}\sum_{j=0}^{k-1}E\left[\int_{j\eta}^{(j+1)\eta}\left|\nabla \bar{U}_{r}\left(Y_{t}\right)-\nabla \bar{U}_{r}\left(Y_{\lfloor t/\eta\rfloor \eta}\right)\right|^{2}\text{d} t\right]\\ &\qquad+\frac{\beta}{2}\sum_{j=0}^{k-1}E\left[\int_{j\eta}^{(j+1)\eta}\left|\nabla \bar{U}_{r}\left(Y_{\lfloor t/\eta\rfloor \eta}\right)-G\left(Y_{\lfloor t/\eta\rfloor \eta},a_{\lfloor t/\eta\rfloor\eta}\right)\right|^{2}\text{d} t\right]\\ &\quad\le \frac{\beta}{2}\frac{\left(d+2\right)\omega_{\nabla U}(r)}{r}\sum_{j=0}^{k-1}E\left[\int_{j\eta}^{(j+1)\eta}\left|Y_{t}-Y_{\lfloor t/\eta\rfloor \eta}\right|^{2}\text{d} t\right]\\ &\qquad+\frac{\beta}{2}\sum_{j=0}^{k-1}E\left[\eta\left|\nabla \bar{U}_{r}\left(Y_{j \eta}\right)-G\left(Y_{j\eta},a_{j\eta}\right)\right|^{2}\right]. \end{align*}

Note that $E[\langle G(Y_{j\eta},a_{j\eta})-\widetilde{G}(Y_{j\eta}), f(Y_{j\eta})\rangle ]=0$ for any measurable $f:\mathbb{R}^{d}\to\mathbb{R}^{d}$ of linear growth since $Y_{j\eta}$ is square integrable by Lemma 5.1 and $\sigma(Y_{(j-1)\eta},a_{(j-1)\eta},B_{j\eta}-B_{(j-1)\eta})$ -measurable, and $a_{j\eta}$ is independent of this $\sigma$ -algebra. For all $t\in[j\eta,(j+1)\eta)$ , by Lemmas 4.4 and 5.1,

\begin{align*} &E\left[\left|Y_{t}-Y_{\lfloor t/\eta\rfloor \eta}\right|^{2}\right]\\ &\quad=E\left[\left|-\left(t-j\eta\right)G\left(Y_{j\eta},a_{j\eta}\right)+\sqrt{2\beta^{-1}}\left(B_{t}-B_{j\eta}\right)\right|^{2}\right]\\ &\quad=\left(t-j\eta\right)^{2}E\left[\left|G\left(Y_{j\eta},a_{j\eta}\right)-\widetilde{G}\left(Y_{j\eta}\right)+\widetilde{G}\left(Y_{j\eta}\right)\right|^{2}\right]+2\beta^{-1}E\left[\left|B_{t}-B_{j\eta}\right|^{2}\right]\\ &\quad\le \left(t-j\eta\right)^{2}\left(2\delta_{\mathbf{v},2}E\left[\left|Y_{j\eta}\right|^{2}\right]+2\delta_{\mathbf{v},0}+E\left[\left|\widetilde{G}\left(Y_{j\eta}\right)\right|^{2}\right]\right)+2\beta^{-1}d\left(t-j\eta\right)\\ &\quad\le 2\left(t-j\eta\right)^{2}\left(\left\|\widetilde{G}\right\|_{\mathbb{M}}^{2}+\delta_{\mathbf{v},0}+\left((\omega_{\widetilde{G}}(1))^{2}+\delta_{\mathbf{v},2}\right)E\left[\left|Y_{j\eta}\right|^{2}\right]\right)+2\beta^{-1}d\left(t-j\eta\right)\\ &\quad\le 2\left(t-j\eta\right)^{2}\left(\left\|\widetilde{G}\right\|_{\mathbb{M}}^{2}+\delta_{\mathbf{v},0}+\left((\omega_{\widetilde{G}}(1))^{2}+\delta_{\mathbf{v},2}\right)\kappa_{\infty}\right)+2\beta^{-1}d\left(t-j\eta\right)\\ &\quad = \! : \, 2\left(t-j\eta\right)^{2}C'+2\beta^{-1}d\left(t-j\eta\right) \end{align*}

and thus

\begin{align*} \sum_{j=0}^{k-1}E\left[\int_{j\eta}^{(j+1)\eta}\left|Y_{t}-Y_{\lfloor t/\eta\rfloor \eta}\right|^{2}\text{d} t\right]\le \left(\frac{2C'\eta^{3}}{3}+\frac{d\eta^{2}}{\beta}\right)k\le \left(\frac{2C'}{3}+\frac{d}{\beta}\right)k\eta^{2}. \end{align*}

We have that

\begin{align*} E\left[\left|\nabla \bar{U}_{r}\left(Y_{j\eta}\right)-G\left(Y_{j\eta},a_{j\eta}\right)\right|^{2}\right] &\le E\left[2\left(\delta_{\mathbf{b},r,2}+\delta_{\mathbf{v},2}\right)\left|Y_{j\eta}\right|^{2}+2\left(\delta_{\mathbf{b},r,0}+\delta_{\mathbf{v},0}\right)\right]\\ &\le 2\delta_{r,2}\kappa_{\infty}+2\delta_{r,0}. \end{align*}

Assumptions (LS1)–(LS4) of Propositions 4.2 and 4.3 are satisfied owing to (A1)–(A6), Lemma 5.1, and the linear growths of $\widetilde{G}(w_{\lfloor \cdot /\eta\rfloor \eta})$ with respect to $\max_{i=0,\ldots,k}|w_{i\eta}|$ and $\nabla \bar{U}_{r}(w_{t})$ with respect to $|w_{t}|$ . Therefore, the data-processing inequality and Proposition 4.3 give

\begin{align*} D(\mu_{k\eta}\|\bar{\nu}_{k\eta}^{r}) &\le \int\log\left(\frac{\text{d} P_{k\eta}}{\text{d} Q_{k\eta}}\right)\text{d} P_{k\eta}\\ &= \frac{\beta}{4}E\left[\int_{0}^{k\eta}\left|\nabla \bar{U}_{r}\left(Y_{t}\right)-G\left(Y_{\lfloor t/\eta\rfloor \eta},a_{\lfloor t/\eta\rfloor\eta}\right)\right|^{2}\text{d} t\right]\\ &\le \frac{(d+2)\omega_{\nabla U}(r)}{r}\left(\frac{C'\beta}{3}+\frac{d}{2}\right)k\eta^{2}+\beta\left(\delta_{r,2}\kappa_{\infty}+\delta_{r,0}\right)k\eta\\ &=\left(\frac{(d+2)\omega_{\nabla U}(r)}{r}\left(\frac{C'\beta}{3}+\frac{d}{2}\right)\eta+\beta\left(\delta_{r,2}\kappa_{\infty}+\delta_{r,0}\right)\right)k\eta\\ &=\left(C_{0}\frac{\omega_{\nabla U}(r)}{r}\eta+\beta\left(\delta_{r,2}\kappa_{\infty}+\delta_{r,0}\right)\right)k\eta. \end{align*}

This is the desired conclusion.

Lemma 5.5 (Lemma 2 of [Reference Raginsky, Rakhlin and Telgarsky31].) Under (A1) and (A4), for almost all $x\in\mathbb{R}^{d}$ ,

\begin{align*} U(x)\ge \frac{m}{3}|x|^{2}-\frac{b}{2}\log3. \end{align*}

Proof. The proof is adapted from Lemma 2 of [Reference Raginsky, Rakhlin and Telgarsky31]. We first fix $c\in(0,1]$ . Since $\{x\in\mathbb{R}^{d} \, : \, x\text{ or }cx\text{ is in the set such that Eq.~(6) does not hold}\}$ is null, for almost all $x\in\mathbb{R}^{d}$ ,

\begin{align*} U(x)&=U(cx)+\int_{0}^{1}\left\langle \nabla U(cx+t(x-cx)),x-cx\right\rangle \text{d} t\\\ &\ge \int_{0}^{1}\left\langle \nabla U((c+t(1-c))x),(1-c)x\right\rangle \text{d} t\\ &=\int_{0}^{1}\frac{1-c}{c+t(1-c)}\left\langle \nabla U((c+t(1-c))x),(c+t(1-c))x\right\rangle \text{d} t\\ &\ge \int_{0}^{1}\frac{1-c}{c+t(1-c)}\left(m(c+t(1-c))^{2}|x|^{2}-b\right) \text{d} t\\ &=\int_{c}^{1}\frac{1}{s}\left(ms^{2}|x|^{2}-b\right) \text{d} s\\ &=\frac{1-c^{2}}{2}m|x|^{2}+b\log c. \end{align*}

Here, $s=c+t(1-c)$ and thus $\text{d} t=(1-c)^{-1}\text{d} s$ . Then $c=1/\sqrt{3}$ yields the conclusion.

Lemma 5.6. Under (A1)–(A4) and (A6), for all $r\in(0,1]$ , we have that

\begin{align*} \chi^{2}(\mu_{0}\|\bar{\pi}^{r})\le 3^{\beta b/2}\left(\frac{3\psi_{2}}{m\beta}\right)^{d/2}\exp\left(\beta\left(2\left\|\nabla U\right\|_{\mathbb{M}}+U_{0}\right)+2\psi_{0}\right). \end{align*}

Proof. The density of $\bar{\pi}^{r}$ is given as $(\text{d}\bar{\pi}^{r}/\text{d} x)(x)=\bar{\mathcal{Z}}^{r}(\beta)^{-1}\mathrm{e}^{-\beta \bar{U}_{r}\left(x\right)}$ and

\begin{align*} \chi^{2}(\mu_{0}\|\bar{\pi}^{r})&=\int_{\mathbb{R}^{d}}\left(\left(\frac{\left(\int_{\mathbb{R}^{d}}\mathrm{e}^{-\Psi(x)}\text{d} x\right)^{-1}\mathrm{e}^{-\Psi(x)}}{\bar{\mathcal{Z}}^{r}(\beta)^{-1}\mathrm{e}^{-\beta \bar{U}_{r}\left(x\right)}}\right)^{2}-1\right)\bar{\mathcal{Z}}^{r}(\beta)^{-1}\mathrm{e}^{-\beta \bar{U}_{r}\left(x\right)}\text{d} x\\ &=\frac{\bar{\mathcal{Z}}^{r}(\beta)}{\int_{\mathbb{R}^{d}}\mathrm{e}^{-\Psi(x)}\text{d} x}\int_{\mathbb{R}^{d}}\mathrm{e}^{\beta \bar{U}_{r}\left(x\right)-\Psi\left(x\right)}\mu_{0}(\text{d} x)-1\\ &\le \frac{\mathrm{e}^{\psi_{0}}\bar{\mathcal{Z}}^{r}(\beta)}{\int_{\mathbb{R}^{d}}\mathrm{e}^{-\psi_{2}|x|^{2}}\text{d} x}\int_{\mathbb{R}^{d}}\mathrm{e}^{\beta\left(\frac{\omega_{\nabla U}(1)}{2}\left|x\right|^{2}+2\left\|\nabla U\right\|_{\mathbb{M}}\left|x\right|+\left\|U\right\|_{L^{\infty}\left(B_{1}\left(\mathbf{0}\right)\right)}\right)-\Psi(x)}\mu_{0}(\text{d} x)\\ &\le \frac{\mathrm{e}^{\psi_{0}}\bar{\mathcal{Z}}^{r}(\beta)}{(\pi/\psi_{2})^{d/2}}\int_{\mathbb{R}^{d}}\mathrm{e}^{\beta\left(\left\|\nabla U\right\|_{\mathbb{M}}\left|x\right|^{2}+2\left\|\nabla U\right\|_{\mathbb{M}}+\left\|U\right\|_{L^{\infty}\left(B_{1}\left(\mathbf{0}\right)\right)}\right)-\Psi(x)}\mu_{0}(\text{d} x)\\ &\le \frac{\bar{\mathcal{Z}}^{r}(\beta)}{(\pi/\psi_{2})^{d/2}}\mathrm{e}^{\beta\left(2\left\|\nabla U\right\|_{\mathbb{M}}+\left\|U\right\|_{L^{\infty}\left(B_{1}\left(\mathbf{0}\right)\right)}\right)+2\psi_{0}} \end{align*}

by Lemma 4.6 and $2|x|\le |x|^{2}/2+2$ . Lemma 5.5, Jensen’s inequality, and the convexity of $\mathrm{e}^{-x}$ yield

\begin{align*} \bar{\mathcal{Z}}^{r}(\beta)&=\int_{\mathbb{R}^{d}}\mathrm{e}^{-\beta \bar{U}_{r}\left(x\right)}\text{d} x\\ &=\int_{\mathbb{R}^{d}}\mathrm{e}^{-\beta \int _{\mathbb{R}^{d}}U(x-y)\rho_{r}(y)\text{d} y}\text{d} x\\ &\le \int_{\mathbb{R}^{d}}\int_{\mathbb{R}^{d}}\mathrm{e}^{-\beta U(x-y)}\text{d} x\rho_{r}(y)\text{d} y\\ &\le \mathrm{e}^{\frac{1}{2}\beta b\log 3}\int_{\mathbb{R}^{d}}\int_{\mathbb{R}^{d}}\mathrm{e}^{-m\beta|x-y|^{2}/3}\text{d} x\rho_{r}(y)\text{d} y\\ &=3^{\beta b/2}\left(3\pi/m\beta\right)^{d/2}, \end{align*}

and we are done.

Lemma 5.7. (Kullback--Leibler divergence of Gibbs distributions.) Under (A1)–(A4) and (A6), we have that

\begin{align*} D\left(\pi\|\bar{\pi}^{r}\right)\le \beta r\omega_{\nabla U}(r). \end{align*}

Proof. The divergence of $\pi$ from $\bar{\pi}^{r}$ is

\begin{align*} D\left(\pi\|\bar{\pi}^{r}\right)&=\frac{1}{\mathcal{Z}(\beta)}\int \exp\left(-\beta U\left(x\right)\right)\log\left[\frac{\bar{\mathcal{Z}}^{r}(\beta)\exp\left(-\beta U\left(x\right)\right)}{\mathcal{Z}(\beta)\exp\left(-\beta \bar{U}_{r}\left(x\right)\right)}\right]\text{d} x\\ &=\frac{\beta}{\mathcal{Z}(\beta)}\int \exp\left(-\beta U\left(x\right)\right)\left(\bar{U}_{r}(x)-U\left(x\right)\right)\text{d} x+\left(\log\bar{\mathcal{Z}}^{r}(\beta)-\log\mathcal{Z}(\beta)\right).\end{align*}

Lemma 4.10 yields

\begin{align*} \frac{\beta}{\mathcal{Z}(\beta)}\int_{\mathbb{R}^{d}} \left(\bar{U}_{r}(x)-U\left(x\right)\right)\mathrm{e}^{-\beta U\left(x\right)}\text{d} x &\le \frac{\beta}{\mathcal{Z}(\beta)}\int_{\mathbb{R}^{d}} \left|\bar{U}_{r}(x)-U\left(x\right)\right|\mathrm{e}^{-\beta U\left(x\right)}\text{d} x\\ &\le \frac{\beta }{\mathcal{Z}(\beta)}\int_{\mathbb{R}^{d}} r\omega_{\nabla U}(r)\mathrm{e}^{-\beta U\left(x\right)}\text{d} x\\ &\le \beta r\omega_{\nabla U}(r).\end{align*}

Jensen’s inequality and Fubini’s theorem give

\begin{align*} \bar{\mathcal{Z}}^{r}\left(\beta\right)&=\int_{\mathbb{R}^{d}}\exp\left(-\beta \int_{\mathbb{R}^{d}}U\left(x-y\right)\rho_{r}\left(y\right)\text{d} y\right)\text{d} x\\ &\le \int_{\mathbb{R}^{d}}\int_{\mathbb{R}^{d}}\exp\left(-\beta U\left(x-y\right)\right)\rho_{r}\left(y\right)\text{d} y\text{d} x\\ &=\int_{\mathbb{R}^{d}}\int_{\mathbb{R}^{d}}\exp\left(-\beta U\left(x-y\right)\right)\text{d} x\rho_{r}\left(y\right)\text{d} y\\ &= \mathcal{Z}\left(\beta\right)\end{align*}

and thus $\log\bar{\mathcal{Z}}^{r}(\beta)-\log\mathcal{Z}(\beta)\le 0$ .

5.4. Proof of Theorem 2.1

We complete the proof of Theorem 2.1.

Proof of Theorem 2.1. We decompose the 2-Wasserstein distance as follows:

\begin{align*} \mathcal{W}_{2}(\mu_{k\eta},\pi)\le \underbrace{\mathcal{W}_{2}(\mu_{k\eta},\bar{\nu}_{k\eta}^{r})}_\textrm{(1)}+\underbrace{\mathcal{W}_{2}(\bar{\nu}_{k\eta}^{r},\bar{\pi}^{r})}_\textrm{(2)}+\underbrace{\mathcal{W}_{2}(\bar{\pi}^{r},\pi)}_\textrm{(3)}.\end{align*}

1. (1) We first consider an upper bound for $\mathcal{W}_{2}(\mu_{k\eta},\bar{\nu}_{k\eta}^{r})$ . Proposition 4.6 gives

   \begin{align*} \mathcal{W}_{2}(\mu_{k\eta},\bar{\nu}_{k\eta}^{r})\le C_{\bar{\nu}_{k\eta}^{r}}\left(D\left(\mu_{k\eta}\|\bar{\nu}_{k\eta}^{r}\right)^{1/2}+\left(\frac{D\left(\mu_{k\eta}\|\bar{\nu}_{k\eta}^{r}\right)}{2}\right)^{1/4}\right),\end{align*}
   where
   \begin{align*} C_{\bar{\nu}_{k\eta}^{r}}\,:\!=\,2\inf_{\lambda>0}\left(\frac{1}{\lambda}\left(\frac{3}{2}+\log\int_{\mathbb{R}^{d}}\mathrm{e}^{\lambda\left|x\right|^{2}}\bar{\nu}_{k\eta}^{r}\left(\text{d} x\right)\right)\right)^{1/2}.\end{align*}
   We fix $\lambda=1\wedge (\beta \bar{m}/4)$ and then Lemma 5.2 leads to
   \begin{align*} C_{\bar{\nu}_{k\eta}^{r}}&\le \frac{1}{\lambda^{1/2}}\left(6+4\log\int_{\mathbb{R}^{d}}\mathrm{e}^{\lambda\left|x\right|^{2}}\bar{\nu}_{k\eta}^{r}\left(\text{d} x\right)\right)^{1/2}\\ &\le \frac{1}{\lambda^{1/2}}\left(6+4\left(\lambda\kappa_{0}+\frac{4\lambda(\bar{b}+d/\beta)}{\bar{m}}+1\right)\right)^{1/2}\\ &\le \left(4\kappa_{0}+\frac{16(\bar{b}+d/\beta)}{\bar{m}}+\frac{10}{1\wedge (\beta \bar{m}/4)}\right)^{1/2}.\end{align*}
   Hence, Lemma 5.4 gives the following bound:
   \begin{align*} \mathcal{W}_{2}(\mu_{k\eta},\bar{\nu}_{k\eta}^{r}) &\le C_{1}\max\left.\left\{x^{1/2},x^{1/4}\right\}\right|_{x=\left(C_{0}\frac{\omega_{\nabla U}(r)}{r}\eta+\beta\left(\delta_{r,2}\kappa_{\infty}+\delta_{r,0}\right)\right)k\eta}.\end{align*}

2. (2) Let us now give a bound for $\mathcal{W}_{2}(\bar{\nu}_{k\eta}^{r},\bar{\pi}^{r})$ . Proposition 4.5 and Lemma 5.6 yield

   \begin{align*} \mathcal{W}_{2}(\bar{\nu}_{k\eta}^{r},\bar{\pi}^{r})&\le \sqrt{2c_\textrm{ P}(\bar{\pi}^{r})\chi^{2}\left(\mu_{0}\|\bar{\pi}^{r}\right)}\exp\left(-\frac{t}{2\beta c_\textrm{ P}(\bar{\pi}^{r})}\right)\\ &\le \sqrt{2c_\textrm{P}(\bar{\pi}^{r})C_{2}}\exp\left(-\frac{t}{2\beta c_\textrm{P}(\bar{\pi}^{r})}\right).\end{align*}

3. (3) Next, we consider a bound for $\mathcal{W}_{2}(\bar{\pi}^{r},\pi)$ . Proposition 4.6 gives

   \begin{align*} \mathcal{W}_{2}(\bar{\pi}^{r},\pi)\le C_{\bar{\pi}^{r}}\left(D\left(\pi\|\bar{\pi}^{r}\right)^{1/2}+\left(\frac{D\left(\pi\|\bar{\pi}^{r}\right)}{2}\right)^{1/4}\right),\end{align*}
   where $C_{\bar{\pi}^{r}}\,:\!=\,2\inf_{\lambda>0}\left(\frac{1}{\lambda}\left(\frac{3}{2}+\log\int_{\mathbb{R}^{d}}\mathrm{e}^{\lambda\left|x\right|^{2}}\bar{\pi}^{r}\left(\text{d} x\right)\right)\right)^{1/2}$ . We fix $\lambda=1\wedge (\beta \bar{m}/4)$ and then Lemmas 5.2 along with Fatou’s lemma leads to
   \begin{align*} C_{\bar{\pi}^{r}} &\le \left(\frac{16(\bar{b}+d/\beta)}{\bar{m}}+\frac{10}{1\wedge (\beta \bar{m}/4)}\right)^{1/2}.\end{align*}
   Lemma 5.7 yields the bound
   \begin{align*} \mathcal{W}_{2}(\bar{\pi}^{r},\pi)\le C_{1}\max\left.\left\{y^{1/2},y^{1/4}\right\}\right|_{y=\beta r\omega_{\nabla U}(r)}.\end{align*}

4. (4) By (1) and (3),

   \begin{align*} \mathcal{W}_{2}(\mu_{k\eta},\bar{\nu}_{k\eta}^{r})+\mathcal{W}_{2}(\bar{\pi}^{r},\pi)&\le C_{1}\left(\max\left\{x^{1/2},x^{1/4}\right\}+\max\left\{y^{1/2},y^{1/4}\right\}\right)\\ &\le C_{1}\left(2\left(x+y\right)^{1/2}+2\left(x+y\right)^{1/4}\right),\end{align*}
   where
   \begin{align*} x=\left(C_{0}\frac{\omega_{\nabla U}(r)}{r}\eta+\beta\left(\delta_{r,2}\kappa_{\infty}+\delta_{r,0}\right)\right)k\eta,\ {y=\beta r\omega_{\nabla U}(r)}.\end{align*}
   Hence, we obtain the desired conclusion.