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The graph is $\mathrm{gph}S=\{(u,v):v\in S(u)\}$. Local graph normals encode the Mordukhovich coderivative and hence the Aubin property. Graph closedness is a local hypothesis in the sensitivity criterion.

In finite dimensions, if the graph is locally closed at $(\bar{u},\bar{v})$, the Aubin property holds exactly when $D^{\ast }S(\bar{u}\mid \bar{v})(0)=\{0\}$. Thus no nonzero horizontal vector in the limiting normal cone may occur. The exact local Lipschitz modulus is the outer norm of this coderivative.

Let $S:{\mathbb{R}}^p\rightrightarrows {\mathbb{R}}^q$ be a set-valued mapping and let $\bar{v}\in S(\bar{u})$. The Aubin property at $(\bar{u},\bar{v})$ means there are neighborhoods $U$ of $\bar{u}$, $V$ of $\bar{v}$, and a finite constant $\kappa \ge 0$ such thatHere ${\mathbb{B}}_q$ is the closed unit ball. Equivalently, every solution $v\in S(u)$ near $\bar{v}$ can be matched to a solution $v^{\prime }\in S(u^{\prime })$ within $\kappa \Vert u-u^{\prime }{\Vert }_2$. The localization is on the left-hand side: the matching point need not be in $V$. The property is also called the Lipschitz-like property.

For sensitivity analysis, let $u$ represent data or perturbations and $S(u)$ the set of feasible or optimal solutions. The Aubin property bounds how far a nearby solution can move when the data change. Taking $u=\bar{u}$, $v=\bar{v}$ also guarantees a nearby solution for each sufficiently small perturbation $u^{\prime }$. It is a stability estimate for a relation, and by itself does not imply uniqueness or differentiability. For a single-valued solution map, it reduces to local Lipschitz continuity.

Yes: the solution map is locally affine, hence has the Aubin property. Keep the regularization parameter $\alpha >0$ fixed and define the active hinge setSince none of the margins vanishes, the optimality condition at the reference data has no fractional weights:For nearby $a$, define the candidateEach map $a\mapsto 1-a_i{x}_I(a)$ is continuous and is nonzero at $\hat{a}$. Because there are finitely many samples, all their signs remain unchanged on a common neighborhood $U$ of $\hat{a}$. Therefore the same set $I$ is active at $x_I(a)$ for every $a\in U$. The hinge-loss optimality weights are still $1$ on $I$ and $0$ off $I$, and the defining equation for $x_I(a)$ proves the subgradient optimality condition. Strong convexity makes this candidate the unique global minimizer. Consequently the solution map of a parametric optimization problem satisfiesThis argument establishes stability of the active pattern without presupposing continuity of the unknown optimizer; continuity is used only for an explicit candidate and then optimality is checked.

For $a,b\in U$, the Cauchy-Schwarz inequality givesThis is the Aubin property with $\kappa =\sqrt{|I|}/(\alpha n)$. In fact this constant is the exact local Lipschitz continuity modulus of the affine branch, since its derivative isThe active-set dependence of this derivative is the active-set sensitivity of hinge-loss minimization. At a vanishing margin the affine-branch argument no longer applies, although failure of this particular argument does not by itself prove failure of the Aubin property.

For the illustration, take $n=2$, $\alpha =1$, and $a=(s,1)$ with $s>0$. The hinge-loss optimality weights giveOn the middle branch the first margin is exactly zero, so that branch lies outside the strict-margin hypothesis. The example makes clear why the local affine conclusion is tied to that hypothesis, and also shows that a zero margin need not destroy local Lipschitz stability.

The solution map sends a data parameter $a$ to the set of minimizers of the associated objective, $S(a)={\mathrm{argmin}}_x{F}_a(x)$. Existence, uniqueness and local stability are distinct questions. Strong convexity gives uniqueness when a minimizer exists; the Aubin property gives a perturbation bound.