Nonsmooth and nonconvex optimization

Optimization problems occur in a large variety of applications in which the desired target is related to some measured data by means of a given model.
Fast and robust numerical optimization methods are increasingly required due to the huge size of the data to be handled nowadays in real-world problems in medicine, computer science and engineering.
The optimization tools we mainly investigate are first-order methods, thanks to their capability of providing a medium accuracy solution with a limited storage requirement and a low computational cost per iteration.
Further speed-ups in the computation are also achieved by implementations of the optimization method on suitable parallel architectures.

Eigenfaces computed in a non-negative matrix factorization problem, which can be reformulated as a constrained least-squares problem.


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[Ultimo aggiornamento: 04/02/2021 08:17:31]