*Last updated: 2023-03-16.*

# tf_quant_finance.math.pde.steppers.douglas_adi.douglas_adi_step

View source Creates a stepper function with Crank-Nicolson time marching scheme. ```python tf_quant_finance.math.pde.steppers.douglas_adi.douglas_adi_step( theta=0.5 ) ``` Douglas ADI scheme is the simplest time marching scheme for solving parabolic PDEs with multiple spatial dimensions. The time step consists of several substeps: the first one is fully explicit, and the following `N` steps are implicit with respect to contributions of one of the `N` axes (hence "ADI" - alternating direction implicit). See `douglas_adi_scheme` below for more details. #### Args: * `theta`: positive Number. `theta = 0` corresponds to fully explicit scheme. The larger `theta` the stronger are the corrections by the implicit substeps. The recommended value is `theta = 0.5`, because the scheme is second order accurate in that case, unless mixed second derivative terms are present in the PDE. #### Returns: Callable to be used in finite-difference PDE solvers (see fd_solvers.py).