Global optimization for sparse solution of least squares problems - ENSTA Bretagne - École nationale supérieure de techniques avancées Bretagne Accéder directement au contenu
Article Dans Une Revue Optimization Methods and Software Année : 2022

Global optimization for sparse solution of least squares problems

Résumé

Finding solutions to least-squares problems with low cardinality has found many applications, including portfolio optimization, subset selection in statistics, and inverse problems in signal processing. Although most works consider local approaches that scale with high-dimensional problems, some others address its global optimization via mixed integer programming (MIP) reformulations. We propose dedicated branch-and-bound methods for the exact resolution of moderate-size, yet difficult, sparse optimization problems, through three possible formulations: cardinality-constrained and cardinality-penalized least-squares, and cardinality minimization under quadratic constraints. A specific tree exploration strategy is built. Continuous relaxation problems involved at each node are reformulated as (Formula presented.) -norm-based optimization problems, for which a dedicated algorithm is designed. The obtained certified solutions are shown to better estimate sparsity patterns than standard methods on simulated variable selection problems involving highly correlated variables. Problem instances selecting up to 24 components among 100 variables, and up to 15 components among 1000 variables, can be solved in less than 1000 s. Unguaranteed solutions obtained by limiting the computing time to 1s are also shown to provide competitive estimates. Our algorithms strongly outperform the CPLEX MIP solver as the dimension increases, especially for quadratically-constrained problems. The source codes are made freely available online.
Fichier principal
Vignette du fichier
OMS_def_23082021.pdf (857.95 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03419540 , version 1 (08-11-2021)
hal-03419540 , version 2 (19-11-2021)

Identifiants

Citer

Ramzi Ben Mhenni, Sébastien Bourguignon, Jordan Ninin. Global optimization for sparse solution of least squares problems. Optimization Methods and Software, 2022, 37 (5), ⟨10.1080/10556788.2021.1977809⟩. ⟨hal-03419540v2⟩
91 Consultations
168 Téléchargements

Altmetric

Partager

Gmail Facebook Twitter LinkedIn More