  In this paper, we consider the robust linear infinite programming problem
$({\rm RLIP}_c) $ defined by \begin{eqnarray*} ({\rm RLIP}_c)\quad &&\inf\;
\langle c,x\rangle \textrm{subject to } &&x\in X,\; \langle x^\ast,x \rangle
\le r ,\;\forall (x^\ast,r)\in\mathcal{U}_t,\; \forall t\in T, \end{eqnarray*}
where $X$ is a locally convex Hausdorff topological vector space, $T$ is an
arbitrary (possible infinite) index set, $c\in X^*$, and $\mathcal{U}_t\subset
X^*\times \mathbb{R}$, $t \in T$ are uncertainty sets.
  We propose an approach to duality for the robust linear problems with convex
constraints $({\rm RP}_c)$ and establish corresponding robust strong duality
and also, stable robust strong duality,
  With the different ways of arranging data from $({\rm RLIP}_c) $, one gets
back to the model $({\rm RP}_c)$ and the (stable) robust strong duality for
$({\rm RP}_c)$ applies. By such a way, nine versions of dual problems for $
({\rm RLIP}_c)$ are proposed. Necessary and sufficient conditions for stable
robust strong duality of these pairs of primal-dual problems are given, which
some cover several known results in the literature while the others, due to the
best knowledge of the authors, are new. Moreover, as by-products, we obtained
from the robust strong duality for variants pairs of primal-dual problems,
several robust Farkas-type results for linear infinite systems with
uncertainty.
  Lastly, as applications, we get the results for robust linear problems with
sub-affine constraints, and to linear infinite problems (i.e., $({\rm RLIP}_c)
$ with the absence of uncertainty).

Optimization and Control