Sign up to save your podcasts
Or
Share A Consensus-Based Algorithm for Non-Convex Multiplayer Games: Quantitative Laplace principle
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
A Consensus-Based Algorithm for Non-Convex Multiplayer Games: Quantitative Laplace principle
This story was originally published on HackerNoon at: https://hackernoon.com/a-consensus-based-algorithm-for-non-convex-multiplayer-games-quantitative-laplace-principle.
A novel algorithm using swarm intelligence to find global Nash equilibria in nonconvex multiplayer games, with convergence guarantees and numerical experiments.
Check more stories related to gaming at: https://hackernoon.com/c/gaming.
You can also check exclusive content about #games, #consensus-based-optimization, #zeroth-order-algorithm, #nonconvex-multiplayer-games, #global-nash-equilibria, #swarm-intelligence, #metaheuristics, #numerical-experiments, and more.
This story was written by: @oligopoly. Learn more about this writer by checking @oligopoly's about page,
and for more stories, please visit hackernoon.com.
This paper is available on arxiv.org/abs/2311.08270 under CC BY 4.0 DEED license. Authors: Enis Chenchene, Hui Huang, Jinniao Qiu, and Hui Chen. Table of Links: 1. Introduction, 2. Global convergence, 3. Numerical experiments, 4. Conclusion, Acknowledgments, and References.
View all episodes
By HackerNoonThis story was originally published on HackerNoon at: https://hackernoon.com/a-consensus-based-algorithm-for-non-convex-multiplayer-games-quantitative-laplace-principle.
A novel algorithm using swarm intelligence to find global Nash equilibria in nonconvex multiplayer games, with convergence guarantees and numerical experiments.
Check more stories related to gaming at: https://hackernoon.com/c/gaming.
You can also check exclusive content about #games, #consensus-based-optimization, #zeroth-order-algorithm, #nonconvex-multiplayer-games, #global-nash-equilibria, #swarm-intelligence, #metaheuristics, #numerical-experiments, and more.
This story was written by: @oligopoly. Learn more about this writer by checking @oligopoly's about page,
and for more stories, please visit hackernoon.com.
This paper is available on arxiv.org/abs/2311.08270 under CC BY 4.0 DEED license. Authors: Enis Chenchene, Hui Huang, Jinniao Qiu, and Hui Chen. Table of Links: 1. Introduction, 2. Global convergence, 3. Numerical experiments, 4. Conclusion, Acknowledgments, and References.