  This work investigates the finite-horizon optimal covariance steering problem
for discrete-time linear systems subject to both additive and multiplicative
uncertainties as well as state and input chance constraints. In particular, a
tractable convex approximation of the optimal covariance steering problem is
developed by tightening the chance constraints and by introducing a suitable
change of variables. The solution of the convex approximation is shown to be a
valid (albeit potentially suboptimal) solution to the original
chance-constrained covariance steering problem.
