  We propose a new \textit{quadratic programming-based} method of approximating
a nonstandard density using a multivariate Gaussian density. Such nonstandard
densities usually arise while developing posterior samplers for unobserved
components models involving inequality constraints on the parameters. For
instance, Chan et al. (2016) provided a new model of trend inflation with
linear inequality constraints on the stochastic trend. We implemented the
proposed quadratic programming-based method for this model and compared it to
the existing approximation. We observed that the proposed method works as well
as the existing approximation in terms of the final trend estimates while
achieving gains in terms of sample efficiency.
