KKT-free constrained optimization and application to river-Flow modelling

This paper explores Augmented Lagrangian methods for derivative-free continuous optimization under constraints. The theoretical analysis proceeds without assuming the existence of derivatives, and the proofs of these results bypass traditional KKT conditions. Numerical experiments demonstrate the fitting of parameters within river flow models. These models address the estimation of hydraulic parameters by minimizing their dispersion, subject to both differential equations and empirical observations. The experiments are situated within the framework of the CRIAB project, which focuses on the study of dam failure.