AL-CoLe: Augmented Lagrangian for Constrained Learning

• URL: http://arxiv.org/abs/2510.20995v2
• Date: Tue, 28 Oct 2025 21:25:00 GMT
• Title: AL-CoLe: Augmented Lagrangian for Constrained Learning
• Authors: Ignacio Boero, Ignacio Hounie, Alejandro Ribeiro,
• Abstract summary: Despite the non-ity of most modern machine learning parameterizations, Lagrangian duality has become a popular tool for addressing constrained learning problems.<n>We demonstrate its effectiveness on constrained classification tasks.
• Score: 79.45233551350152
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the non-convexity of most modern machine learning parameterizations, Lagrangian duality has become a popular tool for addressing constrained learning problems. We revisit Augmented Lagrangian methods, which aim to mitigate the duality gap in non-convex settings while requiring only minimal modifications, and have remained comparably unexplored in constrained learning settings. We establish strong duality results under mild conditions, prove convergence of dual ascent algorithms to feasible and optimal primal solutions, and provide PAC-style generalization guarantees. Finally, we demonstrate its effectiveness on fairness constrained classification tasks.

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