{"ID":3083650,"CreatedAt":"2026-06-05T06:46:15.197025399Z","UpdatedAt":"2026-06-07T09:00:11.459356253Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.06300","arxiv_id":"2606.06300","title":"Multi-ResNets for Subspace Preconditioning in Constrained Optimization","abstract":"We propose MResOpt, a staged residual neural network architecture for constrained optimization problems. Our architecture fits within predict-complete-correct pipelines and decomposes constraint satisfaction by priority through intermediate re-completion and stage-aware losses. The framework enables domain-informed ordered constraint satisfaction which allows the network to utilize ordinal structure when present. Under an idealized infinite-width regime, we show that our design behaves as sequential Gaussian Process regression. On synthetic QP, QCQP, and SOCP benchmarks, the staged architecture improves high-priority constraint satisfaction across convex and non-convex settings. On line-flow-constrained AC optimal power flow, we introduce a physics-motivated constraint ordering and show that MResOpt supports a learned division of labor that keeps iterates on the equality manifold, achieving substantially lower high-priority violation than reprojected baselines while remaining computationally efficient.","short_abstract":"We propose MResOpt, a staged residual neural network architecture for constrained optimization problems. Our architecture fits within predict-complete-correct pipelines and decomposes constraint satisfaction by priority through intermediate re-completion and stage-aware losses. The framework enables domain-informed ord...","url_abs":"https://arxiv.org/abs/2606.06300","url_pdf":"https://arxiv.org/pdf/2606.06300v1","authors":"[\"Merve Karakas\",\"Christopher J. Williams\",\"Emmanuel O. Balogun\",\"Sadegh Sadeghi Tabas\",\"Christian Brown\",\"Nikhil Rao\"]","published":"2026-06-04T15:37:55Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[]","has_code":false}