Randomized derivative-free Milstein algorithm for efficient approximation of solutions of SDEs under noisy information

12/14/2019 ∙ by Paweł M. Morkisz, et al. ∙ 0

We deal with pointwise approximation of solutions of scalar stochastic differential equations in the presence of informational noise about underlying drift and diffusion coefficients. We define a randomized derivative-free version of Milstein algorithm A̅^df-RM_n and investigate its error. We also study lower bounds on the error of an arbitrary algorithm. It turns out that in some case the scheme A̅^df-RM_n is the optimal one. Finally, in order to test the algorithm A̅^df-RM_n in practice, we report performed numerical experiments.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.