We study the unique information function $UI(T:X\setminus Y)$ defined by Bertschinger et al.  within the framework of information decompositions. In particular, we study uniqueness and support of the solutions to the convex optimization problem underlying the definition of $UI$. We identify sufficient conditions for non-uniqueness of solutions with full support in terms of conditional independence constraints and in terms of the cardinalities of $T$, $X$ and $Y$. Our results are based on a reformulation of the first order conditions on the objective function as rank constraints on a matrix of conditional probabilities. These results help to speed up the computation of $UI(T:X\setminus Y)$, most notably when $T$ is binary. Optima in the relative interior of the optimization domain are solutions of linear equations if $T$ is binary. In the all binary case, we obtain a complete picture of where the optimizing probability distributions lie.
unique information, information decomposition