  Let $A$ be a square matrix with a given structure (e.g. real matrix, sparsity
pattern, Toeplitz structure, etc.) and assume that it is unstable, i.e. at
least one of its eigenvalues lies in the complex right half-plane. The problem
of stabilizing $A$ consists in the computation of a matrix $B$, whose
eigenvalues have negative real part and such that the perturbation $\Delta=B-A$
has minimal norm. The structured stabilization further requires that the
perturbation preserves the structural pattern of $A$. We solve this non-convex
problem by a two-level procedure which involves the computation of the
stationary points of a matrix ODE. We exploit the low rank underlying features
of the problem by using an adaptive-rank integrator that follows slavishly the
rank of the solution. We show the benefits derived from the low rank setting in
several numerical examples, which also allow to deal with high dimensional
problems.
