Distributed model predictive control via finite-step control Lyapunov functions

What the math gives to ML

The paper’s transferable asset is a constructive finite-step Lyapunov viewpoint: stability need not decrease at every iteration, provided a measurable certificate decreases after a fixed horizon M. This is useful for neural systems whose single-step updates are non-monotone, including optimizers, recurrent state-space models, and unrolled inference networks. A practical adaptation is to penalize violations of an M-step decrease inequality while explicitly allowing a bounded mismatch term caused by stochastic minibatches, stale activations, or re-optimization. The resulting regularizer can be tested without changing the network architecture and can provide a direct signal for training stability rather than relying only on final loss.