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We consider methods for solving large scale optimization problems based on machine learning. In particular, we specify a class of optimization algorithms using only linear operations and applications of proximal operators, which is general enough to span first-order solvers like Chambolle-Pock. We then apply unsupervised learning to find the best solver in this class for solving the TV-problem in tomography, with constraint on the computation time. Finally, the trained solver is compared to state-of-the-art solvers.
This is poster number 54 in Poster Session