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In this talk, we describe a new active-set algorithmic framework for minimizing a function over the simplex. The method is quite general and encompasses different strategies. We first analyze the convergence of the framework. Then, we explain how to adapt it in order to handle the problem of minimizing a function over the l1-ball. Finally, we report numerical experiments on different classes of instances.
This presentation is part of Minisymposium “MS13 - Optimization for Imaging and Big Data (2 parts)”
organized by: Margherita Porcelli (University of Firenze) , Francesco Rinaldi (University of Padova) .