Alternating projections on cleanly intersecting C^{2,1} submanifolds induce a retraction (second-order under C^{3,1}) on the intersection, with NewtonSLRA as an explicit second-order example.
Alternating projections on nontangential manifolds
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Regularized DDPC formulations are convex relaxations of bi-level identification-control problems, and the new A-DDPC algorithm outperforms prior regularized methods by lowering bias and variance errors.
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Retractions by Alternating Projections
Alternating projections on cleanly intersecting C^{2,1} submanifolds induce a retraction (second-order under C^{3,1}) on the intersection, with NewtonSLRA as an explicit second-order example.
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Regularization in Data-driven Predictive Control: A Convex Relaxation Perspective
Regularized DDPC formulations are convex relaxations of bi-level identification-control problems, and the new A-DDPC algorithm outperforms prior regularized methods by lowering bias and variance errors.