  In this paper, we give some observation of applying modern optimization
methods for functionals describing digital predistortion (DPD) of signals with
orthogonal frequency division multiplexing (OFDM) modulation. The considered
family of model functionals is determined by the class of cascade
Wiener--Hammerstein models, which can be represented as a computational graph
consisting of various nonlinear blocks. To assess optimization methods with the
best convergence depth and rate as a properties of this models family we
multilaterally consider modern techniques used in optimizing neural networks
and numerous numerical methods used to optimize non-convex multimodal
functions. The research emphasizes the most effective of the considered
techniques and describes several useful observations about the model properties
and optimization methods behavior.
