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Anticipating random periodic solutions—I. SDEs with multiplicative linear noise

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journal contribution
posted on 2016-05-16, 11:05 authored by Chunrong Feng, Yue Wu, Huaizhong Zhao
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(R, L²loc (Ω)) using an argument of truncations, the Malliavin calculus, the relative compactness of Wiener–Sobolev spaces in C([0,T],L²(Ω)) and Schauder's fixed point theorem. We finally measurably glue the local solutions together to obtain a global solution in C(R,L²(Ω)). Thus we obtain the existence of a random periodic solution and a periodic measure.

History

School

  • Science

Department

  • Mathematical Sciences

Published in

Journal of Functional Analysis

Volume

271

Issue

2

Pages

365 - 417

Citation

FENG, C., WU, Y. and ZHAO, H., 2016. Anticipating random periodic solutions—I. SDEs with multiplicative linear noise. Journal of Functional Analysis, 271 (2), pp. 365–417.

Publisher

© Elsevier

Version

  • AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

2016-04-27

Publication date

2016-05-09

Notes

This paper was accepted for publication in the journal Journal of Functional Analysis and the definitive published version is available at http://dx.doi.org/10.1016/j.jfa.2016.04.027.

ISSN

0022-1236

Language

  • en

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