Convergence and Representation Learning of Neural Gradient Descent-Ascent for Functional Conditional Moment Equations

The authors study the convergence of gradient descent-ascent (GDA) algorithm and the representation learning of neural networks in solving minimax optimization problems defined over infinite-dimensional function classes, with a focus on functional conditional moment equations.