R. Tyrrell Rockafellar

University of Washington and University of Florida, USA

Stochastic Variational Inequalities in a Dynamical Framework

On Wednesday, June 22nd, 2016 at 15h30

(room 232 - IMPA)
 
Most of the research on stochastic variational inequalities has concentrated on models in which information about the uncertain future is revealed only once. 

Such models are inadequate to cover multistage stochastic programming, where information comes in stages that offer repeated opportunities for recourse decisions. 

That feature can be brought into stochastic variational inequalities by adapting them to a  constraint of nonanticipativity.  

In that way not only stochastic programming but multistage multiagent games can be covered.
 
A particular advantage of this approach is that it generates information price vectors which can be used to decompose the overall problem into a separate problem for each scenario. 

This fits with solution approaches like the progressive hedging algorithm.