Maximal inequalities for stochastic convolutions and pathwise uniform convergence of time discretisation schemes

Abstract

We prove a new Burkholder–Rosenthal type inequality for discrete-time processes taking values in a 2-smooth Banach space. As a first application we prove that if $$(S(t,s))_{0\leqslant s\le t\leqslant T}$$ ( S ( t , s ) ) 0 ⩽ s ≤ t ⩽ T is a $$C_0$$ C 0 -evolution family of contractions on a 2-smooth Banach space X and $$(W_t)_{t\in [0,T]}$$ ( W t ) t ∈ [ 0 , T ] is a cylindrical Brownian motion on a probability space $$(\Omega ,{\mathbb {P}})$$ ( Ω , P ) adapted to some given filtration, then for every $$0<p<\infty $$ 0 < p < ∞ there exists a constant $$C_{p,X}$$ C p , X such that for all progressively measurable processes $$g: [0,T]\times \Omega \rightarrow X$$ g : [ 0 , T ] × Ω → X the process $$(\int _0^t S(t,s)g_s\,\mathrm{d} W_s)_{t\in [0,T]}$$ ( ∫ 0 t S ( t , s ) g s d W s ) t ∈ [ 0 , T ] has a continuous modification and $$\begin{aligned} {\mathbb {E}}\sup _{t\in [0,T]}\Big \Vert \int _0^t S(t,s)g_s\,\mathrm{d} W_s \Big \Vert ^p\leqslant C_{p,X}^p {\mathbb {E}} \Bigl (\int _0^T \Vert g_t\Vert ^2_{\gamma (H,X)}\,\mathrm{d} t\Bigr )^{p/2}. \end{aligned}$$ E sup t ∈ [ 0 , T ] ‖ ∫ 0 t S ( t , s ) g s d W s ‖ p ⩽ C p , X p E ( ∫ 0 T ‖ g t ‖ γ ( H , X ) 2 d t ) p / 2 . Moreover, for $$2\leqslant p<\infty $$ 2 ⩽ p < ∞ one may take $$C_{p,X} = 10 D \sqrt{p},$$ C p , X = 10 D p , where D is the constant in the definition of 2-smoothness for X. The order $$O(\sqrt{p})$$ O ( p ) coincides with that of Burkholder’s inequality and is therefore optimal as $$p\rightarrow \infty $$ p → ∞ . Our result improves and unifies several existing maximal estimates and is even new in case X is a Hilbert space. Similar results are obtained if the driving martingale $$g_t\,\mathrm{d} W_t$$ g t d W t is replaced by more general X-valued martingales $$\,\mathrm{d} M_t$$ d M t . Moreover, our methods allow for random evolution systems, a setting which appears to be completely new as far as maximal inequalities are concerned. As a second application, for a large class of time discretisation schemes (including splitting, implicit Euler, Crank-Nicholson, and other rational schemes) we obtain stability and pathwise uniform convergence of time discretisation schemes for solutions of linear SPDEs $$\begin{aligned} \,\mathrm{d} u_t = A(t)u_t\,\mathrm{d} t + g_t\,\mathrm{d} W_t, \quad u_0 = 0, \end{aligned}$$ d u t = A ( t ) u t d t + g t d W t , u 0 = 0 , where the family $$(A(t))_{t\in [0,T]}$$ ( A ( t ) ) t ∈ [ 0 , T ] is assumed to generate a $$C_0$$ C 0 -evolution family $$(S(t,s))_{0\leqslant s\leqslant t\leqslant T}$$ ( S ( t , s ) ) 0 ⩽ s ⩽ t ⩽ T of contractions on a 2-smooth Banach spaces X. Under spatial smoothness assumptions on the inhomogeneity g, contractivity is not needed and explicit decay rates are obtained. In the parabolic setting this sharpens several know estimates in the literature; beyond the parabolic setting this seems to provide the first systematic approach to pathwise uniform convergence to time discretisation schemes.

Stochastic energy balance climate models with Legendre weighted diffusion and an additive cylindrical Wiener process forcing

“…It coincides with the Burkholder inequality (and therefore it is optimal) as p → ∞. Among other properties, the inequality (79) enables us to obtain the stablity and pointwise uniform convergence of some time discretisation schemes (see [51] for details).…”

“…. , 2 ≤ p < ∞, (79) was obtained in [51], where the positive constant C p is the order O( √ p). It coincides with the Burkholder inequality (and therefore it is optimal) as p → ∞.…”

Stochastic energy balance climate models with Legendre weighted diffusion and a cylindrical Wiener process forcing

“…It coincides with the Burkholder inequality (and therefore it is optimal) as p → ∞. Among other properties, the inequality (79) enables us to obtain the stablity and pointwise uniform convergence of some time discretisation schemes (see [45] for details).…”

“…was obtained in [45], where the positive constant C p is the order O( √ p). It coincides with the Burkholder inequality (and therefore it is optimal) as p → ∞.…”

Comparative Study on Binomial Distribution Content in High School Textbooks

“…; see, e.g., [7,10,12,15,22,24,26]. Because of its wide applications, the numerical methods of stochastic partial differential equations have been extensively studied in the past decades, and by now it is still an active research area; see, e.g., [2,3,5,6,8,9,16,31,34,35].…”