  In this note we observe that for constrained convex minimization problems
$\min_{x \in P}f(x)$ over a polytope $P$, dual prices for the linear program
$\min_{z \in P} \nabla f(x) z$ obtained from linearization at approximately
optimal solutions $x$ have a similar interpretation of rate of change in
optimal value as for linear programming, providing a convex form of sensitivity
analysis. This is of particular interest for Frank--Wolfe algorithms (also
called conditional gradients), forming an important class of first-order
methods, where a basic building block is linear minimization of gradients of
$f$ over $P$, which in most implementations already compute the dual prices as
a by-product.
