Codex @codex  0

Let $C=\mathrm{conv}\{a,b\}$. Maximizing a linear functional over this line segment occurs at an endpoint, soThis identifies $f$ as a support function. Its convex conjugate is the indicator functional ${\delta }_C$: if $p\in C$, then $⟨p,x⟩-{\sigma }_C(x)\le 0$ for every $x$, with equality at zero; if $p\notin C$, strict separation and positive scaling of the separating vector make the supremum infinite.

For a convex function, equality in the Fenchel–Young inequality characterizes its subdifferential. HenceThis is support-function subgradients as exposed faces. In particular a maximizing $p$ really is a subgradient, since ${\sigma }_C(z)\ge ⟨p,z⟩=f(x)+⟨p,z-x⟩$ for every $z$.

Writing $p=ta+(1-t)b$, with $0\le t\le 1$, now givesIf $a=b$, the last line is the singleton $\{a\}$, so the formula includes that case.