Nonconvex optimization and convergence of stochastic gradient descent, and solution of asynchronous game
Optimization and Control
2025-03-06 v2
Abstract
We review convergence and behavior of stochastic gradient descent for convex and nonconvex optimization, establishing various conditions for convergence to zero of the variance of the gradient of the objective function, and presenting a number of simple examples demonstrating the approximate evolution of the probability density under iteration, including applications to both classical two-player and asynchronous multiplayer games
Keywords
Cite
@article{arxiv.2503.02155,
title = {Nonconvex optimization and convergence of stochastic gradient descent, and solution of asynchronous game},
author = {Kevin Buck and Jessica Babyak and Paolo Piersanti and Kevin Zumbrun and Christiane Gallos and Dorothea Gallos},
journal= {arXiv preprint arXiv:2503.02155},
year = {2025}
}