Learning koopman operator under dissipativity constraints

Keita Hara, Masaki Inoue, Noboru Sebe

Research output: Contribution to journalArticlepeer-review


This paper addresses a learning problem for nonlinear dynamical systems with incorporating any specified dissipativity property. The nonlinear systems are described by the Koopman operator, which is a linear operator defined on the infinite-dimensional lifted state space. The problem of learning the Koopman operator under specified quadratic dissipativity constraints is formulated and addressed. The learning problem is in a class of the non-convex optimization problem due to nonlinear constraints and is numerically intractable. By applying the change of variable technique and the convex overbounding approximation, the problem is reduced to sequential convex optimization and is solved in a numerically efficient manner. Finally, a numerical simulation is given, where high modeling accuracy achieved by the proposed approach including the specified dissipativity is demonstrated.

Original languageEnglish
JournalUnknown Journal
Publication statusPublished - 2019 Nov 10


  • Dissipativity
  • Koopman Operator
  • Learning
  • Linear Matrix Inequality

ASJC Scopus subject areas

  • General

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