Analysis of Model and Iteration Dependencies in Distributed Feasible-Point Algorithms for Optimal Control Computation

The problem of computing optimal control inputs is studied for networks of dynamical linear systems, with respect to separable input constraints and a separable quadratic cost over a finite time-horizon. The main results concern network structures for which the iterates of three feasible-point algorithms can be computed exactly on a subsystem-by-subsystem basis with access restricted to local model-data and algorithm-state information. In particular, hop-based network proximity bounds are investigated for algorithms based on projected gradient, random co-ordinate descent and Jacobi iterations, via graph-based characterisations of various aspects of an equivalent static formulation of the optimal control problem.