NLPOpt-Net: A Learning Method for Nonlinear Optimization with Feasibility Guarantees
Nonlinear Parametric Optimization Network (NLPOpt-Net) is an unsupervised learning architecture to solve constrained nonlinear programs (NLP). Given the structure of an NLP, it learns the parametric solution maps with guaranteed constraint satisfaction. The architecture consists of a backbone neural network (NN) followed by a multilayer ($k$-layered) projection. While the NN drives toward optimality through a loss function consisting of a modified Lagrangian augmented with a consistency loss, the projection ensures feasibility by projecting the NN predictions in the original constraint manifold. Instead of typical distance minimization, our projection exploits local quadratic approximatio...
| Authors | Bimol Nath Roy, Rahul Golder, MM Faruque Hasan |
|---|---|
| Primary category | cs.LG |
| Published | 30 April 2026 |
| Last updated at source | 30 April 2026 |
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