Applicability of AAD(Adjoint automatic differentiation) to an indifferentiable function at some point.
I recently learned about Adjoint automatic differentiation(AAD) while studying Monte Carlo Simulation. Through the papers [1], [2], and [3], I reviewed the definition of AAD and examples of various financial engineering such as Basket option, Asian option, American option and CVA based on a fixed-for-floating IRS. Its basic idea is to decompose the process of differentiating into intrinsic functions and operations, thereby increasing the efficiency of the process. Therefore, this method must have differentiation at all steps. One question arises here. Can't this ADD method be applied to a pricing function with an in-differentiable payoff function? For example, I want to apply this method to a range accrual.
I think this method is really efficient. But can't it be applied to functions that are impossible to differentiate, as in the example I presented? I'm sorry for my poor English and expressions.
[1] Fast Greeks by algorithmic differentiation written by Luca Capriotti
[2] Financial Applications of Algorithmic Differentiation written by Chengbo Wang
[3] AAD and least-square Monte Carlo : fast Bermudan-style options and XVA Greeks written by Luca Capriotti, Yupeng Jiang and Andrea Macrina