Superiority of stochastic symplectic methods via the law of iterated logarithm

TL;DR

This paper theoretically explains why stochastic symplectic methods outperform non-symplectic ones by demonstrating their ability to preserve the law of iterated logarithm, which characterizes the asymptotic growth of solution fluctuations.

Contribution

It proves that stochastic symplectic methods asymptotically preserve the law of iterated logarithm, unlike non-symplectic methods, providing a theoretical basis for their superiority.

Findings

Stochastic symplectic methods preserve the law of iterated logarithm asymptotically.

Non-symplectic methods do not preserve this law.

Results apply to linear stochastic oscillator and Schrödinger equation.

Abstract

The superiority of stochastic symplectic methods over non-symplectic counterparts has been verified by plenty of numerical experiments, especially in capturing the asymptotic behaviour of the underlying solution process. How can one theoretically explain this superiority? This paper gives an answer to this problem from the perspective of the law of iterated logarithm, taking the linear stochastic Hamiltonian system in Hilbert space as a test model. The main contribution is twofold. First, by fully utilizing the time-change theorem for martingales and the Borell--TIS inequality, we prove that the upper limit of the exact solution with a specific scaling function almost surely equals some non-zero constant, thus confirming the validity of the law of iterated logarithm. Second, we prove that stochastic symplectic fully discrete methods asymptotically preserve the law of iterated logarithm, but non-symplectic ones do not. This reveals the good ability of stochastic symplectic methods in characterizing the almost sure asymptotic growth of the utmost fluctuation of the underlying solution process. Applications of our results to the linear stochastic oscillator and the linear stochastic Schrodinger equation are also presented.