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Autores principales: Buck, Kevin, Babyak, Jessica, Piersanti, Paolo, Zumbrun, Kevin, Gallos, Christiane, Gallos, Dorothea
Formato: Preprint
Publicado: 2025
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Acceso en línea:https://arxiv.org/abs/2503.02155
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author Buck, Kevin
Babyak, Jessica
Piersanti, Paolo
Zumbrun, Kevin
Gallos, Christiane
Gallos, Dorothea
author_facet Buck, Kevin
Babyak, Jessica
Piersanti, Paolo
Zumbrun, Kevin
Gallos, Christiane
Gallos, Dorothea
contents 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
format Preprint
id arxiv_https___arxiv_org_abs_2503_02155
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Nonconvex optimization and convergence of stochastic gradient descent, and solution of asynchronous game
Buck, Kevin
Babyak, Jessica
Piersanti, Paolo
Zumbrun, Kevin
Gallos, Christiane
Gallos, Dorothea
Optimization and Control
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
title Nonconvex optimization and convergence of stochastic gradient descent, and solution of asynchronous game
topic Optimization and Control
url https://arxiv.org/abs/2503.02155