Just a trivial fact: every polyhedron that is used in linear programming is convex, that is \(Ax \leq b\) is convex, for a matrix \(A\) and a (column) vector \(b\).

**Proof:** Take any \(x', x''\) that satisfy the system of inequalities \(Ax \leq b\).
Then, for \(0 \leq \lambda \leq 1\), we have that \(\lambda Ax' \leq \lambda b\),
that is \(A \lambda x' \leq \lambda b\). Similarly, for \(x''\), we have
that \(A (1-\lambda) x' \leq (1-\lambda) b\). Summing the inequalities, we
get:
\[
A[\lambda x' + (1-\lambda) x''] \leq [\lambda + (1-\lambda)] b = b,
\]
which means that \(\hat{x} = \lambda x' + (1-\lambda) x''\) is again a
solution of the original set of inequalities, thus concluding the argument.