Extremal distributions in information theory and hypothesis testing

Many problems in information theory can be distilled to an optimization problem over a space of probability distributions. The most important examples are in communication theory, where it is necessary to maximize mutual information in order to compute channel capacity, and the classical hypothesis testing problem in which an optimal test is based on the maximization of divergence. Two general classes of optimization problems are considered in this paper: convex and linear programs, where the constraint set is defined by a finite number of moment constraints.