M.C. Rotkowitz and N.C. Martins
On the Closest Quadratically Invariant Constraint
Proceedings of the 48th IEEE Conference on Decision and Control, pp. 1607-1612, December 2009.


Quadratic invariance is a condition which has been shown to allow for optimal decentralized control problems to be cast as convex optimization problems. The condition relates the constraints that the decentralization imposes on the controller to the structure of the plant. In this paper, we consider the problem of finding the closest subset and superset of the decentralization constraint which are quadratically invariant when the original problem is not. We show that this can itself be cast as a convex problem for the case where the controller is subject to delay constraints between subsystems, but that this fails when we only consider sparsity constraints on the controller. For that case, we develop an algorithm that finds the closest superset in a fixed number of steps, and discuss methods of finding a close subset.


In this paper, the algorithm for finding the closest quadratically invariant superset was shown to converge within N^2 iterations, where N is the number of subsystems. This was later reduced to log_2(N). The proof for the improved bound is given in the subsequent TAC paper and IFAC paper.