  In most dynamic networks, it is impractical to measure all of the system
states; instead, only a subset of the states are measured through sensors.
Consequently, and unlike full state feedback controllers, output feedback
control utilizes only the measured states to obtain a stable closed-loop
performance. This paper explores the interplay between the selection of minimal
number of sensors and actuators (SaA) that yield a stable closed-loop system
performance. Through the formulation of the static output feedback control
problem, we show that the simultaneous selection of minimal set of SaA is a
combinatorial optimization problem with mixed-integer nonlinear matrix
inequality constraints. To address the computational complexity, we develop two
approaches: The first approach relies on integer/disjunctive programming
principles, while the second approach is a simple algorithm that is akin to
binary search routines. The optimality of the two approaches is also discussed.
Numerical experiments are included showing the performance of the developed
approaches.
