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• W3179995546 abstract "We introduce a tamed exponential time integrator which exploits linear terms in both the drift and diffusion for Stochastic Differential Equations (SDEs) with a one sided globally Lipschitz drift term. Strong convergence of the proposed scheme is proved, exploiting the boundedness of the geometric Brownian motion (GBM) and we establish order 1 convergence for linear diffusion terms. In our implementation we illustrate the efficiency of the proposed scheme compared to existing fixed step methods and utilize it in an adaptive time stepping scheme. Furthermore we extend the method to nonlinear diffusion terms and show it remains competitive. The efficiency of these GBM based approaches is illustrated by considering some well-known SDE models." @default.
• W3179995546 created "2021-07-19" @default.
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• W3179995546 date "2022-01-01" @default.
• W3179995546 modified "2023-10-05" @default.
• W3179995546 title "Strong convergence of a GBM based tamed integrator for SDEs and an adaptive implementation" @default.
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• W3179995546 doi "https://doi.org/10.1016/j.cam.2021.113704" @default.
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