Anticipating Random Periodic Solutions--I. SDEs with Multiplicative Linear Noise

TL;DR

This paper establishes the existence of random periodic solutions for semilinear stochastic differential equations with multiplicative noise, using advanced stochastic integral equations, Malliavin calculus, and fixed point methods.

Contribution

It introduces a novel approach to identify and construct random periodic solutions for SDEs with multiplicative noise via coupled infinite horizon stochastic integral equations.

Findings

Existence of random periodic solutions for semilinear SDEs with multiplicative noise.

Development of a method to solve localized forward-backward integral equations.

Construction of global solutions by gluing local solutions using measure-theoretic techniques.

Abstract

In this paper, we study the existence of random periodic solutions for semilinear stochastic differential equations. We identify them as solutions of coupled forward-backward infinite horizon stochastic integral equations (IHSIEs), using the "substitution theorem" of stochastic differential equations with anticipating initial conditions. In general, random periodic solutions and the solutions of IHSIEs, are anticipating. For the linear noise case, with the help of the exponential dichotomy given in the multiplicative ergodic theorem, we can identify them as the solutions of infinite horizon random integral equations (IHSIEs). We then solve a localised forward-backward IHRIE in $C(\mathbb{R}, L^2_{loc}(\Omega))$ using an argument of truncations, the Malliavin calculus, the relative compactness of Wiener-Sobolev spaces in $C([0, T], L^2(\Omega))$ and Schauder's fixed point theorem. We finally measurably glue the local solutions together to obtain a global solution in $C(\mathbb{R}, L^2(\Omega))$. Thus we obtain the existence of a random periodic solution and a periodic measure.