  The capacity of 1-D constraints is given by the entropy of a corresponding
stationary maxentropic Markov chain. Namely, the entropy is maximized over a
set of probability distributions, which is defined by some linear requirements.
In this paper, certain aspects of this characterization are extended to 2-D
constraints. The result is a method for calculating an upper bound on the
capacity of 2-D constraints.
  The key steps are: The maxentropic stationary probability distribution on
square configurations is considered. A set of linear equalities and
inequalities is derived from this stationarity. The result is a concave
program, which can be easily solved numerically. Our method improves upon
previous upper bounds for the capacity of the 2-D ``no independent bits''
constraint, as well as certain 2-D RLL constraints.
