SPBM extends classical penalty-barrier methods to stochastic non-convex non-smooth settings via exponential dual averaging and Moreau envelopes, matching baselines with linear overhead up to 10,000 constraints.
Enhanced physics-informed neural networks with augmented lagrangian relaxation method (al-pinns).Neurocomputing, 548: 126424, 2023.Cited on pages 1, 6, 7, and 15
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Stochastic Penalty-Barrier Methods for Constrained Machine Learning
SPBM extends classical penalty-barrier methods to stochastic non-convex non-smooth settings via exponential dual averaging and Moreau envelopes, matching baselines with linear overhead up to 10,000 constraints.