Optimization of the mean-square approximation procedures for iterated Ito stochastic integrals based on multiple Fourier-Legendre series

نویسندگان

چکیده

The article is devoted to optimization of the mean-square approximation procedures for iterated Ito stochastic integrals multiplicities 1 5. mentioned are part strong numerical methods with convergence orders 1.0, 1.5, 2.0, and 2.5 differential equations multidimensional non-commutative noise based on unified Taylor-Ito expansion multiple Fourier-Legendre series converging in sense norm Hilbert space $L_2([t, T]^k)$ $(k=1,\ldots,5).$ In this we use within framework method generalized Fourier series. We show that lengths sequences independent standard Gaussian random variables required 5 can be significantly reduced without loss accuracy these integrals.

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ژورنال

عنوان ژورنال: Journal of Physics: Conference Series

سال: 2021

ISSN: ['1742-6588', '1742-6596']

DOI: https://doi.org/10.1088/1742-6596/1925/1/012010