  We consider an optimization problem related to semi-active damping of
vibrating systems. The main problem is to determine the best damping matrix
able to minimize influence of the input on the output of the system. We use a
minimization criteria based on the $\mathcal{H}_2$ system norm.
  The objective function is non-convex and the associated optimization problem
typically requires a large number of objective function evaluations. We propose
an optimization approach that calculates `interpolatory' reduced order models,
allowing for significant acceleration of the optimization process.
  In our approach, we use parametric model reduction (PMOR) based on the
Iterative Rational Krylov Algorithm, which ensures good approximations relative
to the $\mathcal{H}_2$ system norm, aligning well with the underlying damping
design objectives. For the parameter sampling that occurs within each PMOR
cycle, we consider approaches with predetermined sampling and approaches using
adaptive sampling, and each of these approaches may be combined with three
possible strategies for internal reduction. In order to preserve important
system properties, we maintain second-order structure, which through the use of
modal coordinates, allows for very efficient implementation.
  The methodology proposed here provides a significant acceleration of the
optimization process; the gain in efficiency is illustrated in numerical
experiments.
