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Optimal rate of convergence for approximations of SPDEs with non-regular drift

Oleg Butkovsky, Konstantinos Dareiotis, Máté Gerencsér

TL;DR

The optimal strong rate of convergence is proved without posing any regularity assumption on the non-linear reaction term and the proof relies on stochastic sewing techniques.

Abstract

A fully discrete finite difference scheme for stochastic reaction-diffusion equations driven by a $1+1$-dimensional white noise is studied. The optimal strong rate of convergence is proved without posing any regularity assumption on the non-linear reaction term. The proof relies on stochastic sewing techniques.

Optimal rate of convergence for approximations of SPDEs with non-regular drift

TL;DR

The optimal strong rate of convergence is proved without posing any regularity assumption on the non-linear reaction term and the proof relies on stochastic sewing techniques.

Abstract

A fully discrete finite difference scheme for stochastic reaction-diffusion equations driven by a -dimensional white noise is studied. The optimal strong rate of convergence is proved without posing any regularity assumption on the non-linear reaction term. The proof relies on stochastic sewing techniques.

Paper Structure

This paper contains 13 sections, 23 theorems, 4 equations.

Key Result

Theorem 1.0.1

For any $\varepsilon\in(0,1/2)$, bounded and measurable $b$, and any initial condition of class $\mathcal{C}^{1/2-\varepsilon}(\mathbb{T})$, the forward Euler finite difference approximation of eq:main in classical form converges strongly with rate $1/4-\varepsilon/2$ w.r.t. time and $1/2-\varepsilo

Theorems & Definitions (53)

  • Theorem 1.0.1
  • Remark 1.3.1
  • Theorem 1.3.2
  • Remark 1.3.3
  • Remark 1.3.4
  • Remark 1.3.5
  • Definition 2.1.1
  • Proposition 2.1.2
  • Remark 2.1.3
  • proof
  • ...and 43 more