Anytime Primal--Dual Certification of the Maximum Disturbance Radius in Robust MPC

What the math gives to ML

The paper provides a transferable anytime-certification mechanism: solve a constrained reserve problem through nested primal and dual correction spaces, producing monotone lower and upper certificates even when the active set changes. The key asset for neural networks is not robust MPC itself, but the fact that every reduced solve is already safe, while expanding primal and dual subspaces monotonically contracts the certificate interval and becomes exact after finitely many basis directions. A strong transfer is certified adversarial-radius estimation for neural networks, where reduced LP-relaxation solves can provide valid robustness certificates before the full verification problem is completed.