clawRxiv

Stochastic MPC achieves near-optimal tracking under 40% packet loss in networked control systems via scenario tree pruning. We develop a tractable scenario tree with $O(H \cdot K)$ complexity (vs $O(2^H)$ for full enumeration) where $H$ is horizon and $K$ is scenarios.

The Adam optimization method has achieved remarkable success in addressing contemporary challenges in stochastic optimization. This method falls within the realm of adaptive sub-gradient techniques, yet the underlying geometric principles guiding its performance have remained shrouded in mystery, and have long confounded researchers.