Exact stable relaxations in robust SOS-convex polynomial optimization
In this talk we examine necessary and sufficient conditions for exact stable relaxations of a class of convex polynomial optimization problems in the face of data uncertainty in the constraints. We consider a robust SOS-convex polynomial optimization problem where the constraint data is affinely parameterized and the uncertainty sets are assumed to be bounded spectrahedra. The class of SOS-convex polynomials is a numerically tractable subclass of convex polynomials and, in particular, contains convex quadratic functions and convex separable polynomials. We also show that the relaxation problem can equivalently be reformulated as a semidefinite linear program.
Palabras clave / Keywords: robust optimization SOS-convex polynomial exact stable relaxation
Otros trabajos en la misma sesión
Últimas noticias
-
04/06/18
Certificados -
13/04/18
Resumen del programa y Programa detallado -
22/03/18
Descuentos en medios de trasporte para congresistas y acompañantes -
01/02/18
Ampliación del plazo de tarifa superreducida -
19/01/18
Ampliación de plazos -
15/01/18
Programación para el día 29 de mayo -
15/01/18
Conferenciantes plenarios -
12/01/18
Sede: Palacio de Congresos -
24/12/17
Sesión plenaria en memoria del Profesor Pedro Gil -
24/12/17
Corrección bases del Premio Ramiro Melendreras