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We present an optimization-based approach to stochastic control problems withnonclassical information structures. We cast these problems equivalently asoptimization prob- lems on joint distributions. The resulting problems arenecessarily nonconvex. Our approach to solving them is through convexrelaxation. We solve the instance solved by Bansal and Basar with a particularapplication of this approach that uses the data processing inequality forconstructing the convex relaxation. Using certain f-divergences, we obtain anew, larger set of inverse optimal cost functions for such problems. Insightsare obtained on the relation between the structure of cost functions and ofconvex relaxations for inverse optimal control.

An Optimizer's Approach to Stochastic Control Problems with Nonclassical Information Structures