For 1-norm regularized DPC, non-extreme data trajectories are never needed, the trajectory-specific cost is an atomic norm, and the implicit predictor is a symmetric PWA function whose critical regions scale with the regularization weight.
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On data usage and predictive behavior of data-driven predictive control with 1-norm regularization
For 1-norm regularized DPC, non-extreme data trajectories are never needed, the trajectory-specific cost is an atomic norm, and the implicit predictor is a symmetric PWA function whose critical regions scale with the regularization weight.