The idea of using a multi-agent game, rather than minimizing a single loss function, is already used in GANs - and there it seems to be an very powerful generalization of optimization. Personally I would say the idea is extremely interesting.
GANs have lots of applications, and PCA is useful for various tasks in data-analysis: compression, feature selection, reduced-dimensional modelling. I doubt finding applications will be a problem.
Reliably finding solutions (Nash equilibria), is much harder than optimization for minimum loss however. So I see these being much harder to train than loss-based models.
GANs have lots of applications, and PCA is useful for various tasks in data-analysis: compression, feature selection, reduced-dimensional modelling. I doubt finding applications will be a problem.
Reliably finding solutions (Nash equilibria), is much harder than optimization for minimum loss however. So I see these being much harder to train than loss-based models.