Year: 2024
Type: conference-paper
Abstract: Birkhoff-von Neumann (BvN) decomposition writes a doubly stochastic matrix as a convex combination of permutation matrices. For a given doubly stochastic matrix, the decom-position in general is not unique. In many applications a sparsest decomposition, that is with the smallest number of permutation matrices is of interest. This problem is known to be NP-complete, and heuristics are used to obtain sparse solutions. We propose heuristics based on the well-known orthogonal matching pursuit for sparse BvN decomposition. We experimentally compare our heuristics with the state of the art from the literature and show how our methods advance the known heuristics.
Authors: Damien Lesens, Jérémy E. Cohen, Bora Uçar
Institutions: École Normale Supérieure de Lyon, Laboratoire de l'Informatique du Parallélisme, ROMA: Optimisation des ressources : modèles, algorithmes et ordonnancement, Lyon 1 Université, Centre National de la Recherche Scientifique, Inserm, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé, Institut National des Sciences Appliquées de Lyon, Institut national de recherche en sciences et technologies du numérique
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funder: Agence Nationale de la Recherche
OpenAlex ID: W4392673667