Linearization Techniques for Controlled Piecewise Deterministic Markov Processes; Application to Zubov’s Method

Linearization Techniques for Controlled Piecewise Deterministic Markov Processes; Application to... We aim at characterizing domains of attraction for controlled piecewise deterministic processes using an occupational measure formulation and Zubov’s approach. Firstly, we provide linear programming (primal and dual) formulations of discounted, infinite horizon control problems for PDMPs. These formulations involve an infinite-dimensional set of probability measures and are obtained using viscosity solutions theory. Secondly, these tools allow to construct stabilizing measures and to avoid the assumption of stability under concatenation for controls. The domain of controllability is then characterized as some level set of a convenient solution of the associated Hamilton-Jacobi integral-differential equation. The theoretical results are applied to PDMPs associated to stochastic gene networks. Explicit computations are given for Cook’s model for gene expression. Applied Mathematics and Optimization Springer Journals

Linearization Techniques for Controlled Piecewise Deterministic Markov Processes; Application to Zubov’s Method

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Copyright © 2012 by Springer Science+Business Media, LLC
Mathematics; Systems Theory, Control; Calculus of Variations and Optimal Control; Optimization; Theoretical, Mathematical and Computational Physics; Mathematical Methods in Physics; Numerical and Computational Physics
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