The Hessian Riemannian flow and Newton's method for effective Hamiltonians and Mather measures
KAUST DepartmentApplied Mathematics and Computational Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberOSR-CRG2017-3452
Preprint Posting Date2018-10-08
Permanent link to this recordhttp://hdl.handle.net/10754/662306
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AbstractEffective Hamiltonians arise in several problems, including homogenization of Hamilton-Jacobi equations, nonlinear control systems, Hamiltonian dynamics, and Aubry-Mather theory. In Aubry-Mather theory, related objects, Mather measures, are also of great importance. Here, we combine ideas from mean-field games with the Hessian Riemannian flow to compute effective Hamiltonians and Mather measures simultaneously. We prove the convergence of the Hessian Riemannian flow in the continuous setting. For the discrete case, we give both the existence and the convergence of the Hessian Riemannian flow. In addition, we explore a variant of Newton's method that greatly improves the performance of the Hessian Riemannian flow. In our numerical experiments, we see that our algorithms preserve the non-negativity of Mather measures and are more stable than related methods in problems that are close to singular. Furthermore, our method also provides a way to approximate stationary MFGs.
CitationGomes, D. A., & Yang, X. (2020). The Hessian Riemannian flow and Newton’s method for effective Hamiltonians and Mather measures. ESAIM: Mathematical Modelling and Numerical Analysis, 54(6), 1883–1915. doi:10.1051/m2an/2020036
SponsorsThe authors were supported by King Abdullah University of Science and Technology (KAUST) baseline funds and KAUST OSR-CRG2017-3452.