Analysis of the Identifying Regulation With Adversarial Surrogates Algorithm

Ron Teichner, Ron Meir, Michael Margaliot

Research output: Contribution to journalArticlepeer-review

Abstract

Given a time-series zkk=1N of noisy measured outputs along a single trajectory of a dynamical system, the Identifying Regulation with Adversarial Surrogates (IRAS) algorithm aims to find a non-trivial first integral of the system, that is, a scalar function g such that g(zi)≈g(zj), for all i,j. IRAS has been suggested recently and was used successfully in several learning tasks in models from biology and physics. Here, we give the first rigorous analysis of this algorithm in a specific setting. We assume that the observations admit a linear first integral and that they are contaminated by Gaussian noise. We show that in this case the IRAS iterations are closely related to the self-consistent-field (SCF) iterations for solving a generalized Rayleigh quotient minimization problem. Using this approach, we derive several sufficient conditions guaranteeing local convergence of IRAS to the linear first integral.

Original languageEnglish
Pages (from-to)1
Number of pages1
JournalIEEE Control Systems Letters
DOIs
StateAccepted/In press - 2024

Keywords

  • Biological system modeling
  • Classification algorithms
  • eigenvalue problems
  • Heuristic algorithms
  • learning algorithms
  • Mathematical models
  • Noise measurement
  • Rayleigh quotient
  • ribosome flow model
  • self-consistent-field iteration
  • Trajectory
  • Vectors

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Control and Optimization

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