Initial public release of ENFORCE v1.0.0

ENFORCE is a hard-constrained neural network framework for nonlinear constrained learning. It combines a neural network backbone with AdaNP (Adaptive-depth Neural Projection), an SQP-inspired Gauss-Newton correction module that drives predictions to feasibility with respect to nonlinear equality and inequality constraints at inference time, no constrained solvers needed during training and inference.

Highlights:

• Exact feasibility for affine constraints in a single projection step
• ε-feasibility for general nonlinear constraints with linear convergence
• Inequality constraints handled via a Fischer-Burmeister reformulation
• Train with standard unconstrained optimizers (Adam)