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As financial problems become more complex, traditional numerical methods for BSDEs (such as the [[Monte Carlo method]], [[finite difference method]], etc.) have shown limitations such as high computational complexity and the curse of dimensionality.
#In high-dimensional scenarios, the Monte Carlo method requires numerous simulation paths to ensure accuracy, resulting in lengthy computation times. In particular, for nonlinear BSDEs, the convergence rate is slow, making it challenging to handle complex financial derivative pricing problems. [[File:Pi monte carlo all.gif|thumb|upright=1.3| Monte Carlo method applied to approximating the value of {{pi}}]]
#The finite difference method, on the other hand, experiences exponential growth in the number of computation grids with increasing dimensions, leading to significant computational and storage demands. This method is generally suitable for simple boundary conditions and low-dimensional BSDEs, but it is less effective in complex situations.
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