  Linear transfers between probability distributions were introduced in [5,6]
in order to extend the theory of optimal mass transportation while preserving
the important duality established by Kantorovich. It is shown here that $\{0,
+\infty\}$-valued linear transfers can be characterized by balayage of measures
with respect to suitable cones of functions \`a la Choquet, while general
linear transfers extend balayage theory by requiring the "sweeping out" of
measures to optimize certain cost functionals. We study the dual class of
Kantorovich operators, which are natural and manageable extensions of Markov
operators. It is also an important subclass of capacities, and could be called
"convex functional Choquet capacities," since they play for non-linear maps the
same role that convex envelopes do for arbitrary numerical functions. A
forthcoming paper [7] will study their ergodic properties and their
applications.
