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A second order primal-dual method for nonsmooth convex composite optimization
[article]
2020
arXiv
pre-print
We develop a second order primal-dual method for optimization problems in which the objective function is given by the sum of a strongly convex twice differentiable term and a possibly nondifferentiable convex regularizer. After introducing an auxiliary variable, we utilize the proximal operator of the nonsmooth regularizer to transform the associated augmented Lagrangian into a function that is once, but not twice, continuously differentiable. The saddle point of this function corresponds to
arXiv:1709.01610v2
fatcat:c5ghicwpjrejllihvir7zr7w7i