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