Generating Correlation Matrices with Graph Structures Using Convex Optimization
This work deals with the generation of theoretical correlation matrices with specific sparsity patterns, associated to graph structures. We present a novel approach based on convex optimization, offering greater flexibility compared to existing techniques, notably by controlling the mean of the entry distribution in the generated correlation matrices. This allows for the generation of correlation matrices that better represent realistic data and can be used to benchmark statistical methods for graph inference.
Authors
- Sophie Achard (ORCID: https://orcid.org/0000-0001-9448-1199)
- Kévin Polisano
- Irène Gannaz (ORCID: https://orcid.org/0000-0003-1456-4985)
- Ali Fakhar
Institutions
- Institut polytechnique de Grenoble (FR)
- Centre National de la Recherche Scientifique (FR)
- Laboratoire des Sciences pour la Conception, l'Optimisation et la Production (FR)
- Centre Inria de l'Université Grenoble Alpes (FR)
- Laboratoire Jean Kuntzmann (FR)
- Université Grenoble Alpes (FR)
Publication Details
- Published
- 2025-06-08
- DOI
- https://doi.org/10.1109/ssp64130.2025.11073257
- Citations
- 1
- Primary Topic
- Graph theory and applications
- Type
- article
- Field-Weighted Citation Impact
- 4.09
Funders
- Agence Nationale de la Recherche