In this talk we review some classical algorithms for solving structured convex optimization problems, passing from gradient descent to proximal iterations and going further to modern proximal primal-dual splitting algorithms in the case of more complicated objective functions. We explore connections with constrained optimization, in which additional projections are involved accelerating the performance of the algorithms.
Applications to mean-field games and image processing are included.
>>Luis Briceno
Visuel
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Sans la vignette (utilisé principalement pour les vieux contenus avec une vignette générique...)
oldid
904