 Fermat rule for convex minimization

For a proper convex function finite at $x$, global minimality is equivalent to $0\in \partial f(x)$. This follows immediately from the defining subgradient inequality. Combined with a subdifferential sum rule, it converts a convex minimization problem into an inclusion, for example the equation defining a proximal operator.