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Convergence in Hölder norms with applications to Monte Carlo methods in infinite dimensions. (English) Zbl 1460.65054

Summary: We show that if a sequence of piecewise affine linear processes converges in the strong sense with a positive rate to a stochastic process that is strongly Hölder continuous in time, then this sequence converges in the strong sense even with respect to much stronger Hölder norms and the convergence rate is essentially reduced by the Hölder exponent. Our first application hereof establishes pathwise convergence rates for spectral Galerkin approximations of stochastic partial differential equations. Our second application derives strong convergence rates of multilevel Monte Carlo approximations of expectations of Banach-space-valued stochastic processes.

MSC:

65J05 General theory of numerical analysis in abstract spaces
65C05 Monte Carlo methods
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