Finite dimensional approximations of Hamilton-Jacobi-Bellman equations in spaces of probability measures. (Featured Article in SIAM Journal on Mathematical Analysis)

Andrzej Swiech (Georgia Tech, USA)

04-Feb-2021, 16:30-17:30 (3 years ago)

Abstract: We will discuss how certain Hamilton-Jacobi-Bellman (HJB) equations in spaces of probability measures can be approximated by finite dimensional equations. The most interesting cases are convergence of viscosity solutions of HJB equations corresponding either to deterministic optimal control problems for systems of $n$ particles or to stochastic optimal control problems for systems of $n$ particles with a common noise, to the viscosity solution of a limiting HJB equation in the space of probability measures. The limiting HJB equation is interpreted in its ``lifted" form in a Hilbert space, which has a unique viscosity solution. When the Hamiltonian is convex in the gradient variable and equations are of first order, it can be proved that the viscosity solutions of the finite dimensional problems converge to the value function of a variational problem in $\mathcal{P}_2(\R^d)$ thus providing a representation formula for the solution of the limiting first order HJB equation. The talk will also contain an overview of existing works and various approaches to partial differential equations in abstract spaces, including spaces of probability measures and Hilbert spaces. The talk is based on a joint work with W. Gangbo and S. Mayorga.

Featured Article Authors: W. Gangbo, S. Mayorga, A. Swiech

analysis of PDEs

Audience: researchers in the topic


Seminar In the Analysis and Methods of PDE (SIAM PDE)

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