Congress detail

Authors: 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.

Meeting type: Congreso.

Type of job: Artículo Breve.

Production: Stochastic Descent vs Sample Average in Uncertain Minimax Control Problems.

Scientific meeting: MACI.

Meeting place: Comodoro Rivadavia.

Organizing Institution: ASAMACI.

It's published?: Yes

Publication place: Comodoro Rivadavia

Meeting month: 5

Year: 2017.

Link: here