An active learning algorithm for linear systems attains the minimal sample complexity for accurate identification using ordinary least squares and semidefinite programming with centered excitation.
Finite-time id entification of linear systems: Fundamental limits and optimal algorithms
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Optimal Centered Active Excitation in Linear System Identification
An active learning algorithm for linear systems attains the minimal sample complexity for accurate identification using ordinary least squares and semidefinite programming with centered excitation.