Speaker
Riccardo Zecchina
(Politecnico di Torino)
Description
Optimization under uncertainty deals with the problem of
optimizing stochastic cost functions given some partial
information on their inputs. These problems are extremely
difficult to
solve and yet pervade all areas of technological and natural
sciences. We propose a general approach to solve such
large-scale stochastic
optimization problems and a Survey Propagation based
algorithm that
implements it. As an illustration, we apply our method to
the stochastic
bipartite matching problem, in the two-stage and multi-stage
cases. The
efficiency of our approach, which does not rely on sampling
techniques, allows us to validate the analytical predictions
with large-scale numerical simulations.
(joint work with Fabrizio Altarelli, Alfredo Braunstein and
Abolfazl
Ramezanpour)
