  The main contribution of this paper consists of providing an explicit formula
to compute the Hoffman constant of the argmin mapping in linear optimization.
The work is developed in the context of right-hand side perturbations of the
constraint system as the Hoffman constant is always infinite when we perturb
the objective function coefficients, unless the left-hand side of the
constraints reduces to zero. In our perturbation setting, the argmin mapping is
a polyhedral mapping whose graph is the union of convex polyhedral sets which
assemble in a so nice way that global measures of the stability (Hoffman
constants) can be computed through semilocal and local ones (as Lipschitz upper
semicontinuity and calmness moduli, whose computation has been developed in
previous works). Indeed, we isolate this nice behavior of the graph in the
concept of well-connected polyhedral mappings and, in a first step, the paper
focuses on Hoffman constant for these multifunctions. When confined to the
optimal set, some specifics on directional stability are also presented.
