An Optimizer's Approach to Stochastic Control Problems With Nonclassical Information Structures PROJECT TITLE:An Optimizer's Approach to Stochastic Control Problems With Nonclassical Information StructuresABSTRACT:We have a tendency to present a general optimization-based framework for stochastic management issues with nonclassical info structures. We have a tendency to cast these issues equivalently as optimization problems on joint distributions. The resulting problems are essentially nonconvex. Our approach to solving them is thru convex relaxation . We solve the instance solved by Bansal and Başar (“Stochastic teams with nonclassical information revisited: When is an affine law optimal?”, IEEE Trans. Automatic Management, 1987) with a specific application of this approach that uses the information processing inequality for constructing the convex relaxation. Using bound $f$-divergences, we tend to obtain a new, larger set of inverse optimal price functions for such problems. Insights are obtained on the relation between the structure of cost functions and of convex relaxations for inverse optimal control. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Bounds on Fast Decodability of Space-Time Block Codes, Skew-Hermitian Matrices, and Azumaya Algebras Virtual Reality-Based Navigation Task to Reveal Obstacle Avoidance Performance in Individuals With Visuospatial Neglect
