Recently Lagrangian relaxation has been used to generate convex approximations of the challenging simulation error minimization problem arising in system identification. In this paper, we present a specialized algorithm to optimize the convex bounds generated by Lagrangian relaxation, applicable to linear state-space models. The algorithm demonstrates superior scalability over general-purpose semidefinite programming solvers. In addition, we show empirically that Lagrangian relaxation is more resilient to a biasing effect commonly observed in other identification methods that guarantee model stability.

This paper presents a specialized algorithm for optimizing convex bounds in Lagrangian relaxation for linear state-space models, demonstrating superior scalability and resilience to biasing effects compared to general-purpose semidefinite programming solvers.

Specialized algorithm for identification of stable linear systems using Lagrangian relaxation

Key Takeaway: This paper presents a specialized algorithm for optimizing convex bounds in Lagrangian relaxation for linear state-space models, demonstrating superior scalability and resilience to biasing effects compared to general-purpose semidefinite programming solvers.