Dynamic Stochastic Optimization
Dynamic stochastic optimization problems have the following information constraint: each decision must be a function of the available information at the corresponding time. This can be expressed as a linear constraint involving conditional expectations. This chapter develops the corresponding theory for convex problems with full observation of the state. The resulting optimality system involves a backward costate equation, the control variable being a point of minimum of some Hamiltonian function.