Build and train Lipschitz constrained networks: TensorFlow implementation of k-Lipschitz layers

Lipschitz Neural Networks described in "Sorting Out Lipschitz Function Approximation" (ICML 2019).

Unofficial PyTorch implementation of "GAN-QP: A Novel GAN Framework without Gradient Vanishing and Lipschitz Constraint"

Source code for "Training Generative Adversarial Networks Via Turing Test".

Выпускная квалификационная работа "Реализация численных методов решения негладких экстремальных задач" / Final qualifying work "Implementation of numerical methods for solving nonsmooth extremal problems"

Adaptive nested optimization scheme for Lipschitz continuous functions

We introduce the new concept of (α,L,δ)-relative smoothness (see https://arxiv.org/pdf/2107.05765.pdf) which covers both the concept of relative smoothness and relative Lipschitz continuity. For the corresponding class of problems, we propose some adaptive and universal methods which have optimal estimates of the convergence rate.