  We propose methods to solve time-varying, sensor and actuator (SaA) selection
problems for uncertain cyber-physical systems. We show that many SaA selection
problems for optimizing a variety of control and estimation metrics can be
posed as semidefinite optimization problems with mixed-integer bilinear matrix
inequalities (MIBMIs). Although this class of optimization problems are
computationally challenging, we present tractable approaches that directly
tackle MIBMIs, providing both upper and lower bounds, and that lead to
effective heuristics for SaA selection. The upper and lower bounds are obtained
via successive convex approximations and semidefinite programming relaxations,
respectively, and selections are obtained with a novel slicing algorithm from
the solutions of the bounding problems. Custom branch-and-bound and
combinatorial greedy approaches are also developed for a broad class of systems
for comparison. Finally, comprehensive numerical experiments are performed to
compare the different methods and illustrate their effectiveness.
