A quadratically constrained minimization problem arising from PDE of Monge-Ampère type (Q849146)
From MaRDI portal
| This is the item page for this Wikibase entity, intended for internal use and editing purposes. Please use this page instead for the normal view: A quadratically constrained minimization problem arising from PDE of Monge-Ampère type |
scientific article; zbMATH DE number 5674597
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | A quadratically constrained minimization problem arising from PDE of Monge-Ampère type |
scientific article; zbMATH DE number 5674597 |
Statements
A quadratically constrained minimization problem arising from PDE of Monge-Ampère type (English)
0 references
24 February 2010
0 references
A quadratically constrained eigenvalue minimization problem is considered. These problems arise in the numerical solution of fully nonlinear three dimensional Monge-Ampère type equations, known as the Dirichlet problem for the \(\sigma_2\)-operator. The nonlinear Dirichlet problem with the elliptic \(\sigma_2\)-operator is linearized in a neighborhood of the solution. The theory and a solution technique is developed for the least-squares minimization of the linearized \(\sigma_2\) problem. The minimization subproblem has to be solved many times during the numerical solution of the Monge-Ampère equation. Therefore an efficient numerical technique is required for the solution of minimization problem. It turns out that the proposed algorithm is finite, of complexity \(\mathcal{O}(n^3)\) and requires additionally solving a simple scalar secular equation. As a numerical example, two dimensional minimization to the solution of the Dirichlet problem for the two-dimensional Monge-Ampère equation, is considered. The numerical results indicate the excellent convergence behavior of the algorithm.
0 references
elliptic Monge-Ampère equation
0 references
quadratic constraints
0 references
eigenvalue minimization
0 references
Dirichlet problem
0 references
\(\sigma_2\)-operator
0 references
least-squares minimization
0 references
numerical example
0 references
convergence
0 references
algorithm
0 references
0.8994784
0 references
0.8927204
0 references
0.8922956
0 references
0.8890971
0 references
0.8889988
0 references
0.88765633
0 references
0.88124776
0 references
0.8808366
0 references