Fitting second-order cone constraints to microbial growth data
| Type | : | ACL |
|---|---|---|
| Nature | : | Production scientifique |
| Au bénéfice du Laboratoire | : | Oui |
| Statut de publication | : | Publié |
| Année de publication | : | 2022 |
| Auteurs (5) | : | TANG S KRICHEN Emna RAPAPORT Alain PASSEPORT Elodie TAYLOR Josh,a |
| Revue scientifique | : | Journal of Process Control |
| Volume | : | 118 |
| Fascicule | : | |
| Pages | : | 165-169 |
| DOI | : | 10.1016/j.jprocont.2022.08.018 |
| URL | : | https://www.sciencedirect.com/science/article/pii/S0959152422001603 |
| Abstract | : | Second-order cone programming is a highly tractable convex optimization class. In this paper, we fit general second-order cone constraints to data. This is of use when one must solve large-scale, nonlinear optimization problems, but modeling is either impractical or does not lead to second-order cone or otherwise tractable constraints. Our motivating application is biochemical process optimization, in which we seek to fit second-order cone constraints to microbial growth data. The fitting problem is nonconvex. We solve it using the concave–convex procedure, which takes the form of a sequence of second-order cone programs. We validate our approach on simulated and experimental microbial growth data, and compare its performance with conventional nonlinear least-squares fitting. |
| Mots-clés | : | Second-order cone programming, Microbial growth, Conic fitting, Concave–convex procedure |
| Commentaire | : | - |
| Tags | : | - |
| Fichier attaché | : | - |
| Citation | : |
Tang S, Krichen E, Rapaport A, Passeport E, Taylor JA (2022) Fitting second-order cone constraints to microbial growth data. J Process Contr 118: 165-169 | doi: 10.1016/j.jprocont.2022.08.018
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