Stochastic Penalty-Barrier Methods for Constrained Machine Learning

In three linesNew SPBM method for constrained optimization in deep learning. Combines penalty methods, barrier methods, and exponential dual averaging to handle non-convexity and non-smoothness. Demonstrates effectiveness on fairness, physics-informed networks, and symbolic knowledge integration with linear overhead up to 10k constraints.

Summary generated by Claude — human-verified