  Simulations of large scale dynamical systems in multi-query or real-time
contexts require efficient surrogate modelling techniques, as e.g. achieved via
Model Order Reduction (MOR). Recently, symplectic methods like the complex
singular value decomposition (cSVD) or the SVD-like decomposition have been
developed for preserving Hamiltonian structure during MOR. In the current
contribution, we show how symplectic structure preserving basis generation can
be made more efficient with randomized matrix factorizations. We present a
randomized complex SVD (rcSVD) algorithm and a randomized SVD-like (rSVD-like)
decomposition. We demonstrate the efficiency of the approaches with numerical
experiments on high dimensional systems.
