BibTeX
@ARTICLE{
         Goloubentsev2020Aad,
       author = "Goloubentsev, Dmitri and Lakshtanov, Evgeny",
       title = "Automatic adjoint differentiation for gradient descent and model calibration",
       journal = "International Journal of Wavelets, Multiresolution and Information Processing",
       volume = "0",
       number = "0",
       pages = "2040004",
       year = "2020",
       doi = "10.1142/S0219691320400044",
       url = "https://doi.org/10.1142/S0219691320400044",
       eprint = "https://doi.org/10.1142/S0219691320400044",
       abstract = "In this work, we discuss the Automatic Adjoint Differentiation (AAD) for functions
         of the form $G=\frac{1}{2} \sum_{i=1}^m (Ey_i-C_i)^2$ which often appear in the
         calibration of stochastic models. We demonstrate that it allows a perfect Single Input Multiple Data
         (SIMD) parallelization and provides its relative computational cost. In addition, we demonstrate
         that this theoretical result is in concordance with numerical experiments."
}