  Given pointwise samples of an unknown function belonging to a certain model
set, one seeks in Optimal Recovery to recover this function in a way that
minimizes the worst-case error of the recovery procedure. While it is often
known that such an optimal recovery procedure can be chosen to be linear, e.g.
when the model set is based on approximability by a subspace of continuous
functions, a construction of the procedure is rarely available. This note
uncovers a practical algorithm to construct a linear optimal recovery map when
the approximation space is a Chevyshev space of dimension at least three and
containing the constant functions.
