Global optimization for low-dimensional switching linear regression and bounded-error estimation
This provides a scalable solution with guaranteed global optimality for hybrid system identification, addressing a known bottleneck in the field.
The paper tackles the nonconvex problems of switching linear regression and bounded-error estimation in hybrid system identification by developing global optimization algorithms with certificates of optimality, achieving higher accuracy than convex relaxations with reasonable computational burden.
The paper provides global optimization algorithms for two particularly difficult nonconvex problems raised by hybrid system identification: switching linear regression and bounded-error estimation. While most works focus on local optimization heuristics without global optimality guarantees or with guarantees valid only under restrictive conditions, the proposed approach always yields a solution with a certificate of global optimality. This approach relies on a branch-and-bound strategy for which we devise lower bounds that can be efficiently computed. In order to obtain scalable algorithms with respect to the number of data, we directly optimize the model parameters in a continuous optimization setting without involving integer variables. Numerical experiments show that the proposed algorithms offer a higher accuracy than convex relaxations with a reasonable computational burden for hybrid system identification. In addition, we discuss how bounded-error estimation is related to robust estimation in the presence of outliers and exact recovery under sparse noise, for which we also obtain promising numerical results.