  In variational signal processing and machine learning problems, loss
functions and linear operators are typically aggregated as an average of
composite terms. We propose an alternative formulation using proximal
comixtures, an operation that combines functions and linear operators in such a
way that the proximity operator of the resulting function is computable
explicitly. The benefits of comixture formulations are illustrated through
image recovery and machine learning applications.
