On gradient based descent algorithms for joint diagonalization of matrices - Signaux et Images
Communication Dans Un Congrès Année : 2024

On gradient based descent algorithms for joint diagonalization of matrices

Erik Troedsson
  • Fonction : Auteur
Marcus Carlsson
  • Fonction : Auteur
  • PersonId : 876203

Résumé

Joint diagonalization of collections of matrices, i.e. the problem of finding a joint set of approximate eigenvectors, is an important problem that appears in many applicative contexts. It is commonly formulated as finding the minimizer, over the set of all possible bases, for a certain non-convex functional that measures the size of off-diagonal elements. Many approaches have been studied in the literature, some of the most popular ones working with approximations of this cost functional. In this work, we deviate from this philosophy and instead propose to directly attempt to find a minimizer making use of the gradient and Hessian of the original functional. Our main contributions are as follows. First, we design and study gradient descent and conjugate gradient algorithms. Second, we show that the intricate geometry of the functional makes it beneficial to change basis at each iteration, leading to faster convergence. Third, we conduct large sets of numerical experiments that indicate that our proposed descent methods yield competitive results when compared to popular methods such as WJDTE.
Fichier principal
Vignette du fichier
ErikEUSIPCO2024.pdf (967.31 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04699707 , version 1 (17-09-2024)

Identifiants

  • HAL Id : hal-04699707 , version 1

Citer

Erik Troedsson, Marcus Carlsson, Herwig Wendt. On gradient based descent algorithms for joint diagonalization of matrices. 32nd European Signal Processing Conference (EUSIPCO 2024), Aug 2024, Lyon, France. à paraître. ⟨hal-04699707⟩
0 Consultations
0 Téléchargements

Partager

More