Il giorno 10 Febbraio, alle ore 10.30, presso il Dipartimento di Matematica e Applicazioni dell'Università di Milano-Bicocca, via Cozzi, 55, Edificio U5, aula 2109 (secondo piano),
il Dott. Andrea Cosso, Politecnico di Milano, terrà un seminario dal titolo "Randomization method and backward SDEs for optimal control of partially observed path-dependent stochastic systems"
Abstract: In the present talk we introduce a general methodology, which we refer to as the randomization method, firstly developed for classical Markovian control problem in the paper: I. Kharroubi and H. Pham "Feynman-Kac representation for Hamilton-Jacobi-Bellman IPDE", Ann. Probab., 2015. As it is well-known, the dynamic programming method is the standard methodology implemented for the study of classical Markovian control problems, which allows to relate the value function to the Hamilton-Jacobi-Bellman equation through the so-called dynamic programming principle. The key feature of the dynamic programming method is that the knowledge of the value function allows, at least in principle, to find an optimal control for the problem. This very powerful and well-known methodology breaks down (in the sense that it can not be directly implemented in a standard way) when we face control problems which present the following additional features: partial observation, path-dependence, delay in the control. On the other hand, the randomization method can be quite easily generalized and adapted to these more general control problems. The aim of the talk is to illustrate this latter point, starting with the presentation of the fundamental ideas of the randomization method. The talk is based on a joint work in progress with E. Bandini, M. Fuhrman, H. Pham. Tutti gli interessati sono invitati a partecipare. Cordiali saluti, Federica Masiero