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I am using Python for solving a large size system of stochastic differential equations (SDE) of the form $\rm{d}\textbf{x}=A\textbf{x}\rm{d}t+b\rm{d}W$, where A and b are a matrix and vector respectively of suitable size and $\rm{d}W$ represents a scalar noise. In my case, the vector $\textbf{x}$ could have at least 800 elements. I found the library sdeint but in their Github readme file they claim that the library is not intended for speed, otherwise Julia seems to be suitable for solving this kind of problems optimally but before migrating my code to Julia I want to give a try to Python. That is why I want to ask what are the most fast libraries for solving SDE systems in Python. Thanks.

Camilo160
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  • Consider these two scipy functions. In general, for speed in Python look for options using numpy, scipy, pandas etc. – J.G. Jun 14 '22 at 08:43
  • I agree that ivp_solve helps to speed up the solution of ODEs problems but I am not sure if it can takes account of the fact that $\rm{d}W$ is of the order of $\sqrt{\rm{d}t}$, which is one of the relevant considerations of my SDE. – Camilo160 Jun 14 '22 at 16:47
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    Good point. On second thoughts, this is better. – J.G. Jun 14 '22 at 16:49

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