Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

Exact Sampling of Determinantal Point Processes without Eigendecomposition

Abstract : Determinantal point processes (DPPs) enable the modelling of repulsion: they provide diverse sets of points. This repulsion is encoded in a kernel K that we can see as a matrix storing the similarity between points. The usual algorithm to sample DPPs is exact but it uses the spectral decomposition of K, a computation that becomes costly when dealing with a high number of points. Here, we present an alternative exact algorithm that avoids the eigenvalues and the eigenvectors computation and that is, for some applications, faster than the original algorithm.
Document type :
Preprints, Working Papers, ...
Complete list of metadatas

Cited literature [21 references]  Display  Hide  Download
Contributor : Claire Launay <>
Submitted on : Wednesday, May 16, 2018 - 4:34:35 PM
Last modification on : Thursday, July 2, 2020 - 5:17:18 PM
Document(s) archivé(s) le : Monday, September 24, 2018 - 5:58:27 PM


  • HAL Id : hal-01710266, version 2
  • ARXIV : 1802.08429


Claire Launay, Bruno Galerne, Agnès Desolneux. Exact Sampling of Determinantal Point Processes without Eigendecomposition. 2018. ⟨hal-01710266v2⟩



Record views


Files downloads