The optimal uniform approximation of systems of stochastic differential equations

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The truncated Euler-Maruyama method for stochastic differential equations (2015, background): “Methods with variable stepsize also attract a lot of attention [5, 28, 35, 37].”
Loss of regularity for Kolmogorov equations (2012, unknown): “, [65, 4, 31, 30, 12, 54, 55, 56, 45] and the references therein.”
An Euler-type method for the strong approximation of the Cox–Ingersoll–Ross process (2012, methodology): “Hence we obtain the optimal strong convergence rate for the approximation of SDEs with Lipschitz coefficients, see [27].”
Antithetic Multilevel Monte Carlo Estimation for Multidimensional SDEs (2013, methodology): “Using this interpolant, we have the result [7]”
On the performance of the Euler–Maruyama scheme for SDEs with discontinuous drift coefficient (2018, background): “The corresponding error rates can not be improved in general, see [8, 10, 20] for the case q ∈ [1,∞) and [8, 19] for the case q = ∞.”
Optimal pointwise approximation of SDEs based on brownian motion at discrete points (2004, methodology): “For a proof of Lemma 10 under condition (A), we refer to M¨uller-Gronbach (2002b).”
Strong convergence of an explicit numerical method for SDEs with nonglobally Lipschitz continuous coefficients (2010, unknown): “For a lower bound of this type of convergence, the reader is referred to Theorem 3 in M¨uller-Gronbach [29] (see also Hofmann, M¨uller-Gronbach and Ritter [13]).”