Privacy concerns spark the desire to analyze largescale interconnected systems in a distributed fashion, i.e. without a central entity having global model knowledge. Two different approaches are presented to analyze stability of interconnected linear time-invariant systems with limited model knowledge.
The two algorithms implement sufficient stability conditions and require information exchange only with direct neighbors thus reducing the need to share model data widely and ensuring privacy. The first algorithm is based on an M-matrix condition, the second one on Lyapunov inequalities. Both algorithms rely on distributed optimization using a dual decomposition approach. Numerical investigations are used to validate both approaches.