TY - JOUR
T1 - A Least-Squares/Relaxation Method for the Numerical Solution of the Three-Dimensional Elliptic Monge–Ampère Equation
AU - Caboussat, Alexandre
AU - Glowinski, Roland
AU - Gourzoulidis, Dimitrios
N1 - Funding Information:
This work has been supported by the Swiss National Science Foundation (Grant SNF 165785), and the US National Science Foundation (Grant NSF DMS-0913982).
PY - 2018/10/1
Y1 - 2018/10/1
N2 - In this article, we address the numerical solution of the Dirichlet problem for the three-dimensional elliptic Monge–Ampère equation using a least-squares/relaxation approach. The relaxation algorithm allows the decoupling of the differential operators from the nonlinearities. Dedicated numerical solvers are derived for the efficient solution of the local optimization problems with cubicly nonlinear equality constraints. The approximation relies on mixed low order finite element methods with regularization techniques. The results of numerical experiments show the convergence of our relaxation method to a convex classical solution if such a solution exists; otherwise they show convergence to a generalized solution in a least-squares sense. These results show also the robustness of our methodology and its ability at handling curved boundaries and non-convex domains.
AB - In this article, we address the numerical solution of the Dirichlet problem for the three-dimensional elliptic Monge–Ampère equation using a least-squares/relaxation approach. The relaxation algorithm allows the decoupling of the differential operators from the nonlinearities. Dedicated numerical solvers are derived for the efficient solution of the local optimization problems with cubicly nonlinear equality constraints. The approximation relies on mixed low order finite element methods with regularization techniques. The results of numerical experiments show the convergence of our relaxation method to a convex classical solution if such a solution exists; otherwise they show convergence to a generalized solution in a least-squares sense. These results show also the robustness of our methodology and its ability at handling curved boundaries and non-convex domains.
KW - Least-squares method
KW - Mixed finite element method
KW - Monge–Ampère equation
KW - Newton methods
KW - Nonlinear constrained minimization
UR - http://www.scopus.com/inward/record.url?scp=85044277644&partnerID=8YFLogxK
U2 - 10.1007/s10915-018-0698-6
DO - 10.1007/s10915-018-0698-6
M3 - Journal article
AN - SCOPUS:85044277644
SN - 0885-7474
VL - 77
SP - 53
EP - 78
JO - Journal of Scientific Computing
JF - Journal of Scientific Computing
IS - 1
ER -