  The strong converse for a coding theorem shows that the optimal asymptotic
rate possible with vanishing error cannot be improved by allowing a fixed
error. Building on a method introduced by Gu and Effros for centralized coding
problems, we develop a general and simple recipe for proving strong converse
that is applicable for distributed problems as well. Heuristically, our proof
of strong converse mimics the standard steps for proving a weak converse,
except that we apply those steps to a modified distribution obtained by
conditioning the original distribution on the event that no error occurs. A key
component of our recipe is the replacement of the hard Markov constraints
implied by the distributed nature of the problem with a soft information cost
using a variational formula introduced by Oohama. We illustrate our method by
providing a short proof of the strong converse for the Wyner-Ziv problem and
strong converse theorems for interactive function computation, common
randomness and secret key agreement, and the wiretap channel; the latter three
strong converse problems were open prior to this work.
