On Convex Functions and the Finite Element Method
Many problems of theoretical and practical interest involve finding a convex or concave function.For instance, optimization problems such as finding the projection on the convex functions in $H^k(Omega)$, or some problems in economics.In the continuous setting and assuming smoothness, the convexity...
| Autores: | , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2009 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/84278 |
| Acceso en línea: | http://hdl.handle.net/11336/84278 |
| Access Level: | acceso abierto |
| Palabra clave: | Finite Element Method Optimization Problems Convex Functions Adaptive Meshes https://purl.org/becyt/ford/1.1 https://purl.org/becyt/ford/1 |
| Sumario: | Many problems of theoretical and practical interest involve finding a convex or concave function.For instance, optimization problems such as finding the projection on the convex functions in $H^k(Omega)$, or some problems in economics.In the continuous setting and assuming smoothness, the convexity constraints may be given locally by asking the Hessian matrix to be positive semidefinite, but in making discrete approximations two difficulties arise: the continuous solutions may be not smooth, and an adequate discrete version of the Hessian must be given.In this paper we propose a finite element description of the Hessian, and prove convergence under very general conditions, even when the continuous solution is not smooth, working on any dimension, and requiring a linear number of constraints in the number of nodes.Using semidefinite programming codes, we show concrete examples of approximations to optimization problems. |
|---|