On non-polynomial lower error bounds for adaptive strong approximation of SDEs

DOI10.1016/j.jco.2017.04.002zbMath1396.65012arXiv1609.08073OpenAlexW2962873919MaRDI QIDQ2402415

Larisa Yaroslavtseva

Publication date: 7 September 2017

Published in: Journal of Complexity (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1609.08073

zbMATH Keywords

stochastic differential equationsadaptive methodsstrong approximationlower error boundsslow convergence ratesmooth coefficients

Mathematics Subject Classification ID

Numerical solutions to stochastic differential and integral equations (65C30) Complexity and performance of numerical algorithms (65Y20)

Related Items (10)

A note on strong approximation of SDEs with smooth coefficients that have at most linearly growing derivatives ⋮ On the performance of the Euler-Maruyama scheme for SDEs with discontinuous drift coefficient ⋮ Strongly asymptotically optimal schemes for the strong approximation of stochastic differential equations with respect to the supremum error ⋮ Convergence of the Euler-Maruyama method for multidimensional SDEs with discontinuous drift and degenerate diffusion coefficient ⋮ On the regularisation of the noise for the Euler-Maruyama scheme with irregular drift ⋮ On arbitrarily slow convergence rates for strong numerical approximations of Cox-Ingersoll-Ross processes and squared Bessel processes ⋮ Lower error bounds for strong approximation of scalar SDEs with non-Lipschitzian coefficients ⋮ On the Euler-Maruyama scheme for SDEs with bounded variation and Hölder continuous coefficients ⋮ Strong convergence for explicit space-time discrete numerical approximation methods for stochastic Burgers equations ⋮ Numerical methods for conservation laws with rough flux

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