One to beat them all: "RYU'' -- a unifying framework for the construction of safe balls

Tran, Thu-Le, Elvira, Clément, Dang, Hong-Phuong, Herzet, Cédric

arXiv.org Machine Learning 

In this paper, we put forth a novel framework (named ``RYU'') for the construction of ``safe'' balls, i.e. regions that provably contain the dual solution of a target optimization problem. We concentrate on the standard setup where the cost function is the sum of two terms: a closed, proper, convex Lipschitz-smooth function and a closed, proper, convex function. The RYU framework is shown to generalize or improve upon all the results proposed in the last decade for the considered family of optimization problems.

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