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Communication dans un congrès

Sparse Branch and Bound for Exact Optimization of L0-Norm Penalized Least Squares

Abstract : We propose a global optimization approach to solve ℓ 0 -norm penalized least-squares problems, using a dedicated branch-and-bound methodology. A specific tree search strategy is built, with branching rules inspired from greedy exploration techniques. We show that the subproblem involved at each node can be evaluated via ℓ 1 -norm-based optimization problems with box constraints, for which an active-set algorithm is built. Our method is able to solve exactly moderate-size, yet difficult, sparse approximation problems, without resorting to mixed-integer programming (MIP) optimization. In particular, it outperforms the generic MIP solver CPLEX.
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https://hal.archives-ouvertes.fr/hal-02564594
Contributeur : Sébastien Bourguignon <>
Soumis le : mardi 5 mai 2020 - 22:23:25
Dernière modification le : mardi 20 octobre 2020 - 10:32:07

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Ramzi Ben Mhenni, Sébastien Bourguignon, Marcel Mongeau, Jordan Ninin, Hervé Carfantan. Sparse Branch and Bound for Exact Optimization of L0-Norm Penalized Least Squares. ICASSP 2020, IEEE International Conference on Acoustics, Speech and Signal Processing, May 2020, Barcelona, Spain. pp.ISBN: 978-1-5090-6632-2, ⟨10.1109/ICASSP40776.2020.9053870⟩. ⟨hal-02564594⟩

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