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