Adjoint Algorithmic Differentiation (AAD) offers an efficient and robust alternative to computing sensitivities compared to the traditional bump-and-revalue approach (finite difference). However its implementation into large existing quant analytics libraries is often considered challenging.
Xcelerit provides training to give clients solid background in the theoretical, software and computational aspects of adjoint algorithmic differentiation (AAD). Xcelerit trainers share their unique expertise to help clients AAD-enable large in-house quantitative finance analytics libraries (typically multi-millions lines of code), while respecting memory constraints, and achieving large performance gains.
In this paper we will zoom on Algorithmic Differentiation as an efficient and robust alternative to compute sensitivities. We cover the theory and then focus on practical examples. The paper further gives guidelines on how to cope with the memory requirements, handle parallelisation, and how to incorporate external library functions.
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