Detalle del congreso
Autores: Aragone, L.S.; Gianatti, J.; Lotito, P.A.; Parente, L.A.
Resumen: We propose a stochastic descent algorithm for solving uncertain minimax control problems. At each iterate, the scheme randomly draws a sample point from the underlying probability space, computes a feasible direction and performs an Armijo step in order to obtain the next iterate. On some simple examples, we compare its performance with a descent algorithm based on sample average approximations, obtaining promissory numerical results.
Tipo de reunión: Congreso.
Tipo de trabajo: Artículo Breve.
Producción: Stochastic Descent vs Sample Average in Uncertain Minimax Control Problems.
Reunión científica: MACI.
Lugar: Comodoro Rivadavia.
Institución organizadora: ASAMACI.
Publicado: Sí
Lugar publicación: Comodoro Rivadavia
Mes de reunión: 5
Año: 2017.
Página web: aquí