Scalable Incremental Robustness Analysis of Neural Network Feedback Systems
Semidefinite programming (SDP) certificates for feedback systems containing deep neural networks (NNs) typically scale with the total number of neurons, whereas small-gain tests are scalable but can be highly conservative. This paper develops a unified and scalable framework for incremental robust stability and performance analysis of feedback interconnections involving high-dimensional NNs and unmodeled dynamics. By combining a structured decomposition of the full-order SDP condition with scalable Lipschitz constant estimation algorithms, we derive reduced verification conditions that certify incremental convergence and incremental $\ell_2$-gain bounds. The dimensions of the resulting co...
| Authors | Zichen Wang, Peter Seiler, Geir Dullerud et al. |
|---|---|
| Primary category | math.OC |
| Published | 18 September 2026 |
| Last updated at source | 18 September 2026 |
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