Optimal Centered Active Excitation in Linear System Identification

Excerpt

arXiv:2604.05518v2 Announce Type: replace-cross Abstract: We propose an active learning algorithm for linear system identification with optimal centered noise excitation. Notably, our algorithm, based on ordinary least squares and semidefinite programming, attains the minimal sample complexity while allowing for efficient computation of an estimate of a system matrix. More specifically, we first establish lower bounds of the sample complexity for any active learning algorithm to attain the presc