30 de mayo de 2023 a 2 de junio de 2023 Ciencias Naturales, Exactas y Ténicas
Facultad de Matemática y Computación
America/Havana zona horaria

Optimality conditions in optimization under uncertainty

No programado
20m
Facultad de Matemática y Computación

Facultad de Matemática y Computación

Ponente

Christiane Tammer (University of Halle-Wittenberg)

Descripción

Most optimization problems involve uncertain data due to measurement errors, unknown future developments and modeling approximations. Stochastic optimization assumes that the uncertain parameter is probabilistic. An other approach is called robust optimization which expects the uncertain parameter to belong to a set that is known prior. In this talk, we consider scalar optimization problems under uncertainty with infinite scenario sets. We apply methods from vector optimization in general spaces, set-valued optimization and scalarization techniques to derive necessary optimality conditions for solutions of robust optimization problems.

Keywords: Robust Optimization, Nonlinear Scalarization, Vector Optimization, Set-valued Optimization, Stochastic Optimization, necessary optimality conditions

Autor primario

Christiane Tammer (University of Halle-Wittenberg)

Materiales de la presentación

Todavía no hay materiales.