Abstract
This paper is concerned with certain phase transition phenomena arising in a family of stochastic ergodic control problems having real parameter \(\beta \). We show that the large time behavior of the optimal diffusion changes drastically in the vicinity of some critical value \(\beta ={\beta _{c}}\). Specifically, the optimal diffusion is recurrent for \(\beta <{\beta _{c}}\), while it is transient for \(\beta >{\beta _{c}}\). We also investigate the large time behavior of the optimal diffusion for \(\beta ={\beta _{c}}\) which turns out to be different from the previous two cases and more subtle. Our proof is based on the Lyapunov method giving analytical criteria for recurrence and transience of diffusions. The key lies in the analysis of solutions to the associated viscous Hamilton–Jacobi equation with bounded inward drift. In particular, a refined version of the gradient estimate for solutions to viscous Hamilton–Jacobi equations plays a substantial role.
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Supported in part by JSPS KAKENHI Grant Number 18K03343.
This article is part of the section “Viscosity solutions - Dedicated to Hitoshi Ishii on the award of the 1st Kodaira Kunihiko Prize” edited by Kazuhiro Ishige, Shigeaki Koike, Tohru Ozawa, Senjo Shimizu.
Appendix
Appendix
In this appendix, we prove Theorem A.1 below, which is a slight refinement of [12, Théorème IV.1]. This kind of gradient estimates have been used in [3, 7,8,9,10] to obtain a bound of \(|D\phi |\). Here we establish an estimate which enables one to obtain the decay rate of \(|D\phi (x)|\) as \(|x|\rightarrow \infty \).
Let \(U\subset {{\mathbf {R}}^{d}}\) be a domain. We consider the elliptic equation
where \(g\in C^1(U;{{\mathbf {R}}^{d}})\), \(G\in C^1(U\times {{\mathbf {R}}^{d}})\), and \(f\in C^1(U)\). We assume that G satisfies the following condition:
(G) There exist some \(m>1\), \(\kappa >0\), and \({M_1},{M_2},{N_1},{N_2}:U\rightarrow [0,\infty )\) such that, for all \(x\in U\) and \(p\in {{\mathbf {R}}^{d}}\),
Theorem A.1
Let (G) hold. Then, there exists some \(K>0\) depending only on d, m, and \(\kappa \) such that, for any solution \(\phi \in C^3(U)\) of (A.1) and \(x_0\in U\), \(r>0\) with \(B_r(x_0)\subset U\),
where \(z_\pm :=\max \{\pm z,0\}\) for \(z\in {\mathbf {R}}\).
Proof
We set \(w:=(1/2)|D\phi |^2\). Then, \(Dw=(D^2\phi )( D\phi )\) and
where \({\text {tr}}(A)\) denotes the trace of a square matrix A. Since first and second terms of the right-hand side of (A.2) can be estimated as
and
respectively, we see by plugging them into (A.2) that
Now, let \(\zeta \in C^2(U)\) be any cut-off function such that \(\zeta \equiv 1\) in \(B_{1/2}\), \({\text {supp}}\zeta \subset B_1\), and \(0\le \zeta \le 1\) in \(B_1\). We set
for \(x\in U\), where \(\gamma >1\) is some constant which will be determined later. Since \(u\equiv 0\) outside \(B_r(x_0)\), we observe that u attains its positive maximum at a point \({\hat{x}}\in B_r(x_0)\). In particular, we have \(u({\hat{x}})=\xi ({\hat{x}}) w({\hat{x}})>0\), \(Du({\hat{x}})=\xi ({\hat{x}}) Dw({\hat{x}})+w({\hat{x}})D\xi ({\hat{x}})=0\), and \(\Delta u({\hat{x}})\le 0\), which also implies that
We now set \(Z:=\xi ({\hat{x}})|D\phi ({\hat{x}})|^{2m}\) and find an upper bound of Z. To this end, we first evaluate \(\Delta u\) at \(x={\hat{x}}\). In what follows, we drop \({\hat{x}}\) for the simplicity of notation. Then,
We next estimate the value of \(\xi \Delta w\) at \(x={\hat{x}}\). Since \(0\le \xi \le \xi ^{\delta }\le 1\) for any \(0\le \delta \le 1\) and \(2\xi |Dw|=|D\phi |^2|D\xi |\) at \(x={\hat{x}}\), we see in view of (A.3) that
Plugging this into the previous inequality, we obtain
where \(K>0\) is some constant depending only on d and \(\kappa \). In the following, we denote by K various constants depending only on d, \(\kappa \), and m.
Now, we recall Young’s inequality to see that, for any \(L>0\) and \(\varepsilon >0\),
where \(C_\varepsilon >0\) is a constant depending only on \(\varepsilon \) and m. Then, we have
We finally find upper bounds for \(\xi ^{-\frac{1}{m}}|D\xi |\), \(\xi ^{-\frac{m+1}{2m}}|D\xi |\), and \(\xi ^{-\frac{1}{m}}|\Delta \xi |\). Since \(\xi ^{-\frac{1}{m}}<\xi ^{-\frac{m+1}{2m}}\), it suffices to consider \(\xi ^{-\theta }|D\xi |\) and \(\xi ^{-\theta }|\Delta \xi |\) for \(\theta :=(m+1)/(2m)\). By choosing \(\gamma :=4m/(m-1)\), which is equivalent to \(\gamma -2-\gamma \theta =0\), we see by direct computations that
Hence, there exists some \(C>0\) depending only on m, \(\sup _{B_1}|D\zeta |\), and \(\sup _{B_1}|\Delta \zeta |\) such that, at \({\hat{x}}\),
Since \(\xi \equiv 1\) in \(B_{r/2}(x_0)\), we see that
which implies the desired estimate. Hence, we have completed the proof. \(\square \)
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Ichihara, N. Phase transitions arising in stochastic ergodic control associated with viscous Hamilton–Jacobi equations with bounded inward drift. SN Partial Differ. Equ. Appl. 2, 15 (2021). https://doi.org/10.1007/s42985-021-00072-0
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DOI: https://doi.org/10.1007/s42985-021-00072-0
Keywords
- Stochastic ergodic control
- Viscous Hamilton–Jacobi equation
- Generalized principal eigenvalue
- Recurrence and transience