  This paper presents a novel approach for solving the pole placement and
eigenstructure assignment problems through data-driven methods. By using
open-loop data alone, the paper shows that it is possible to characterize the
allowable eigenvector subspaces, as well as the set of feedback gains that
solve the pole placement problem. Additionally, the paper proposes a
closed-form expression for the feedback gain that solves the eigenstructure
assignment problem. Finally, the paper discusses a series of optimization
problems aimed at finding sparse feedback gains for the pole placement problem.
