Numerical Solution of Free Stochastic Differential Equations
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866910779011235840 |
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| author | Schluechtermann, Georg Wibmer, Michael |
| author_facet | Schluechtermann, Georg Wibmer, Michael |
| contents | This paper derives a free analog of the Euler-Maruyama method (fEMM) to numerically approximate solutions of free stochastic differential equations (fSDEs). Simply speaking fSDEs are stochastic differential equations in the context of non-commutative random variables (e.g. large random matrices). By applying the theory of multiple operator integrals we derive a free Itô formula from Taylor expansion of operator valued functions. Iterating the free Itô formula allows to motivate and define fEMM. Then we consider weak and strong convergence in the fSDE setting and prove strong convergence order of $\frac{1}{2}$ and weak convergence order of ${1}$. Numerical examples support the theoretical results and show solutions for equations where no analytical solution is known. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_00348 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Numerical Solution of Free Stochastic Differential Equations Schluechtermann, Georg Wibmer, Michael Probability 46L53, 46L54, 60H10, 65C30 This paper derives a free analog of the Euler-Maruyama method (fEMM) to numerically approximate solutions of free stochastic differential equations (fSDEs). Simply speaking fSDEs are stochastic differential equations in the context of non-commutative random variables (e.g. large random matrices). By applying the theory of multiple operator integrals we derive a free Itô formula from Taylor expansion of operator valued functions. Iterating the free Itô formula allows to motivate and define fEMM. Then we consider weak and strong convergence in the fSDE setting and prove strong convergence order of $\frac{1}{2}$ and weak convergence order of ${1}$. Numerical examples support the theoretical results and show solutions for equations where no analytical solution is known. |
| title | Numerical Solution of Free Stochastic Differential Equations |
| topic | Probability 46L53, 46L54, 60H10, 65C30 |
| url | https://arxiv.org/abs/2210.00348 |