Fitting second-order cone constraints to microbial growth data

Shuyao Tan, Emna Krichen, Alain Rapaport, Elodie Passeport, Josh A. Taylor

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)165-169
Number of pages5
JournalJournal of Process Control
StatePublished - Oct 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Computer Science Applications
  • Industrial and Manufacturing Engineering


  • Concave–convex procedure
  • Conic fitting
  • Microbial growth
  • Second-order cone programming


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