Generalized Convexity and Smoothness via Conjugate Duality, with Applications to Deep Network Training

Stochastic gradient descent (SGD) and its variants train deep neural networks (DNNs) effectively in practice, yet classical optimization theory does not fully explain this success: conventional analyses rest on differentiability, convexity, and smoothness, assumptions that DNN objectives routinely v