New algorithms for second order model reduction based on Krylov projections

We consider structure preserving dimension reduction of second order dynamical systems. Two new Krylov projection based algorithms are proposed which work good and fast in experiments. Both algorithms are suitable for large scale settings. They have some numerical advantages as well compared to existing algorithms in terms of time cost and eliminant of some ill-conditioning.