shape-constraints

This paper introduces neural slack variables, a primal-side approach that converts constraint enforcement into a regression problem by coupling the primary network with a jointly learned auxiliary network, achieving zero violations on monotonicity and convexity tests and enabling arbitrage-free learning of volatility surfaces.