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Semidenite Approximations of Invariant Measures for Polynomial Systems

Victor Magron(victor.magron***at***univ-grenoble-alpes.fr)
Marcelo Forets(marcelo.forets***at***utec.edu.uy)
Didier Henrion(henrion***at***laas.fr)

Abstract: We consider the problem of approximating numerically the moments and the supports of measures which are invariant with respect to the dynamics of continuousand discrete-time polynomial systems, under semialgebraic set constraints. First, we address the problem of approximating the density and hence the support of an invariant measure which is absolutely continuous with respect to the Lebesgue measure. Then, we focus on the approximation of the support of an invariant measure which is singular with respect to the Lebesgue measure. Each problem is handled through an appropriate reformulation into a linear optimization problem over measures, solved in practice with two hierarchies of nitedimensional semidenite moment-sum-of-square relaxations, also called Lasserre hierachies. Under specic assumptions, the rst Lasserre hierarchy allows to approximate the moments of an absolutely continuous invariant measure as close as desired and to extract a sequence of polynomials converging weakly to the density of this measure. The second Lasserre hierarchy allows to approximate as close as desired in the Hausdor metric the support of a singular invariant measure with the level sets of the Christoel polynomials associated to the moment matrices of this measure. We also present some application examples together with numerical results for several dynamical systems admitting either absolutely continuous or singular invariant measures.

Keywords: invariant measures, dynamical systems, polynomial optimization, semidefinite programming, moment-sum-of-square relaxations

Category 1: Linear, Cone and Semidefinite Programming (Semi-definite Programming )

Category 2: Nonlinear Optimization (Systems governed by Differential Equations Optimization )

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Entry Submitted: 07/17/2018
Entry Accepted: 07/17/2018
Entry Last Modified: 07/17/2018

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