Efficiently and Effectively Recognizing Toricity of Steady State Varieties - INRIA - Institut National de Recherche en Informatique et en Automatique Accéder directement au contenu
Article Dans Une Revue Mathematics in Computer Science Année : 2021

Efficiently and Effectively Recognizing Toricity of Steady State Varieties

Résumé

We consider the problem of testing whether the points in a complex or real variety with non-zero coordinates form a multiplicative group or, more generally, a coset of a multiplicative group. For the coset case, we study the notion of shifted toric varieties which generalizes the notion of toric varieties. This requires a geometric view on the varieties rather than an algebraic view on the ideals. We present algorithms and computations on 129 models from the BioModels repository testing for group and coset structures over both the complex numbers and the real numbers. Our methods over the complex numbers are based on Gröbner basis techniques and binomiality tests. Over the real numbers we use first-order characterizations and employ real quantifier elimination. In combination with suitable prime decompositions and restrictions to subspaces it turns out that almost all models show coset structure. Beyond our practical computations, we give upper bounds on the asymptotic worst-case complexity of the corresponding problems by proposing single exponential algorithms that test complex or real varieties for toricity
Fichier principal
Vignette du fichier
Grigoriev2021_Article_EfficientlyAndEffectivelyRecog.pdf (690.71 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03438165 , version 1 (21-11-2021)

Licence

Paternité

Identifiants

Citer

Dima Grigoriev, Alexandru Iosif, Hamid Rahkooy, Thomas Sturm, Andreas Weber. Efficiently and Effectively Recognizing Toricity of Steady State Varieties. Mathematics in Computer Science, 2021, 15 (2), pp.199 - 232. ⟨10.1007/s11786-020-00479-9⟩. ⟨hal-03438165⟩
37 Consultations
51 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More