Strong convergence of compensated split-step theta methods for SDEs with jumps under monotone condition

DOI10.1016/j.amc.2018.04.002zbMath1429.65024OpenAlexW2892344267MaRDI QIDQ2007649

Chao Yue

Publication date: 22 November 2019

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2018.04.002

zbMATH Keywords

strong convergencelocal Lipschitz conditionglobal Lipschitz conditioncompensated split-step theta methodsmonotone conditions

Mathematics Subject Classification ID

Ordinary differential equations and systems with randomness (34F05) Computational methods for stochastic equations (aspects of stochastic analysis) (60H35) Numerical solutions to stochastic differential and integral equations (65C30)

Related Items (2)

Compensated projected Euler-Maruyama method for stochastic differential equations with superlinear jumps ⋮ Strong convergence of the split-step backward Euler method for stochastic delay differential equations with a nonlinear diffusion coefficient

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