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    Large-scale optimization: new active-set methods and application in unsupervised learning

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    In this thesis, new methods for large-scale non-linear optimization are presented. In particular, an active-set algorithm for bound-constrained optimization is first proposed, characterized by the use of a suitable active-set estimate that enables us to update separately the variables estimated active and those estimated non-active at each iteration. Then, this approach is extended to solve minimization problems over the unit simplex and to the minimization of a function over the l1-ball. In the last chapter, a data filtering strategy for cluster analysis is proposed, based on solving a least squares problem with l0-norm regularization. Finally, for all the proposed methods, a theoretical analysis is carried out and numerical results are provided

    What we should learn from pandemic publishing

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    Repository accompanying the Nature Human Behavior comment article by Satyaki Sikdar, Sara Venturini, Marie-Laure Charpignon, Sagar Kumar, Francesco Rinaldi, Francesco Tudisco, Santo Fortunato, and Maimuna S. Majumder

    What we should learn from pandemic publishing

    No full text
    Repository accompanying the Nature Human Behavior comment article by Satyaki Sikdar, Sara Venturini, Marie-Laure Charpignon, Sagar Kumar, Francesco Rinaldi, Francesco Tudisco, Santo Fortunato, and Maimuna S. Majumder

    The Race to COVID-19 Research

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    Repository accompanying the Nature Human Behavior comment article by Satyaki Sikdar, Sara Venturini, Marie-Laure Charpignon, Sagar Kumar, Francesco Rinaldi, Francesco Tudisco, Santo Fortunato, and Maimuna S. Majumder

    What we should learn from pandemic publishing

    No full text
    Repository accompanying the Nature Human Behavior comment article by Satyaki Sikdar, Sara Venturini, Marie-Laure Charpignon, Sagar Kumar, Francesco Rinaldi, Francesco Tudisco, Santo Fortunato, and Maimuna S. Majumder
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