 Sensitivity analysis in convex perturbation duality

An optimal dual variable $y^{\ast }\in \partial p(0)$ bounds the effect of perturbing a convex value function:If $p$ is finite convex near zero, $p^{\prime }(0;d)={\max }_{y\in \partial p(0)}⟨y,d⟩$. A singleton subdifferential gives differentiability and a first-order expansion. For an upper-bound constraint relaxed by $z$, the derivative equals the negative of the nonnegative Lagrange multiplier; relaxing the bound can decrease the minimum value.