On Explicit Milstein-type Scheme for Mckean-Vlasov Stochastic Differential Equations with Super-linear Drift Coefficient

04/02/2020
by   Chaman Kumar, et al.
0

We develop an explicit Milstein-type scheme for McKean-Vlasov stochastic differential equations using the notion of derivative with respect to measure introduced by Lions and discussed in <cit.>. The drift coefficient is allowed to grow super-linearly in the space variable. Further, both drift and diffusion coefficients are assumed to be only once differentiable in variables corresponding to space and measure. The rate of strong convergence is shown to be equal to 1.0 without using Itô's formula for functions depending on measure. The challenges arising due to the dependence of coefficients on measure are tackled and our findings are consistent with the analogous results for stochastic differential equations.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/20/2022

The backward Euler-Maruyama method for invariant measures of stochastic differential equations with super-linear coefficients

The backward Euler-Maruyama (BEM) method is employed to approximate the ...
research
06/23/2022

Stochastic Langevin Differential Inclusions with Applications to Machine Learning

Stochastic differential equations of Langevin-diffusion form have receiv...
research
09/17/2019

On Explicit Tamed Milstein-type scheme for Stochastic Differential Equation with Markovian Switching

We propose a new tamed Milstein-type scheme for stochastic differential ...
research
04/23/2021

Stochastic differential equations with irregular coefficients: mind the gap!

Numerical methods for stochastic differential equations with non-globall...
research
03/09/2011

Information Theoretic Limits on Learning Stochastic Differential Equations

Consider the problem of learning the drift coefficient of a stochastic d...
research
05/21/2021

Error Bounds of the Invariant Statistics in Machine Learning of Ergodic Itô Diffusions

This paper studies the theoretical underpinnings of machine learning of ...

Please sign up or login with your details

Forgot password? Click here to reset