all AI news
Accelerated Proximal Alternating Gradient-Descent-Ascent for Nonconvex Minimax Machine Learning. (arXiv:2112.11663v3 [cs.LG] UPDATED)
cs.LG updates on arXiv.org arxiv.org
Alternating gradient-descent-ascent (AltGDA) is an optimization algorithm
that has been widely used for model training in various machine learning
applications, which aim to solve a nonconvex minimax optimization problem.
However, the existing studies show that it suffers from a high computation
complexity in nonconvex minimax optimization. In this paper, we develop a
single-loop and fast AltGDA-type algorithm that leverages proximal gradient
updates and momentum acceleration to solve regularized nonconvex minimax
optimization problems. By identifying the intrinsic Lyapunov function of this …
arxiv gradient gradient-descent learning machine machine learning minimax