  In projection-based model order reduction, a reduced-order approximation of
the original full-order system is obtained by projecting it onto a reduced
subspace that contains its dominant characteristics. The problem of
frequency-weighted H2-optimal model order reduction is to construct a local
optimum in terms of the H2-norm of the weighted error transfer function. In
this paper, a projection-based model order reduction algorithm is proposed that
constructs reduced-order models that nearly satisfy the first-order optimality
conditions for the frequency-weighted H2-optimal model order reduction problem.
It is shown that as the order of the reduced model is increased, the deviation
in the satisfaction of the optimality conditions reduces further. Numerical
methods are also discussed that improve the computational efficiency of the
proposed algorithm. Three numerical examples are presented to demonstrate the
efficacy of the proposed algorithm.
