An efficient weak Euler-Maruyama type approximation scheme of very high dimensional SDEs by orthogonal random variables

DOI10.1016/j.matcom.2021.03.010OpenAlexW3138196709MaRDI QIDQ2664765

Jirô Akahori, Takashi Sawai, Tomooki Yuasa, Masahiro Kinuya

Publication date: 18 November 2021

Published in: Mathematics and Computers in Simulation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2001.05640

zbMATH Keywords

stochastic differential equationsMonte Carlo methodItô-Taylor expansionWagner-Platen expansionweak rate of convergenceEuler-Maruyama schemeshigh dimensional simulation

Mathematics Subject Classification ID

Numerical analysis (65-XX) Probability theory and stochastic processes (60-XX)

Related Items (1)

Higher-order error estimates of the discrete-time Clark-Ocone formula

Uses Software

• MersenneTwister

Cites Work

• Unnamed Item
• The numerical approximation of stochastic partial differential equations
• Cubature on Wiener space for McKean-Vlasov SDEs with smooth scalar interaction
• Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations
• Mean field games
• Monte Carlo simulation of nonlinear diffusion processes
• Higher-order implicit strong numerical schemes for stochastic differential equations
• Monte Carlo simulation of nonlinear diffusion processes. II
• Rate of convergence of a particle method to the solution of the McKean-Vlasov equation
• Some problems in the simulation of nonlinear diffusion processes
• Conversion of mersenne twister to double-precision floating-point numbers
• Semi-implicit Euler-Maruyama approximation for noncolliding particle systems
• Cubature on Wiener space
• Overcoming the order barrier in the numerical approximation of stochastic partial differential equations with additive space–time noise
• Mersenne twister
• Weak rate of convergence for an Euler scheme of nonlinear SDE’s
• Deep backward schemes for high-dimensional nonlinear PDEs
• Solving high-dimensional partial differential equations using deep learning
• On the Walsh Functions

This page was built for publication: An efficient weak Euler-Maruyama type approximation scheme of very high dimensional SDEs by orthogonal random variables