  We consider minimizing $f(x) = \mathbb{E}[f(x,\omega)]$ when $f(x,\omega)$ is
possibly nonsmooth and either strongly convex or convex in $x$. (I) Strongly
convex. When $f(x,\omega)$ is $\mu-$strongly convex in $x$, we propose a
variable sample-size accelerated proximal scheme (VS-APM) and apply it on
$f_{\eta}(x)$, the ($\eta$-)Moreau smoothed variant of
$\mathbb{E}[f(x,\omega)]$; we term such a scheme as (m-VS-APM). We consider
three settings. (a) Bounded domains. In this setting, VS-APM displays linear
convergence in inexact gradient steps, each of which requires utilizing an
inner (SSG) scheme. Specifically, mVS-APM achieves an optimal oracle complexity
in SSG steps; (b) Unbounded domains. In this regime, under a weaker assumption
of suitable state-dependent bounds on subgradients, an unaccelerated variant
mVS-PM is linearly convergent; (c) Smooth ill-conditioned $f$. When $f$ is
$L$-smooth and $\kappa = L/\mu \ggg 1$, we employ mVS-APM where increasingly
accurate gradients $\nabla_x f_{\eta}(x)$ are obtained by VS-APM. Notably,
mVS-APM displays linear convergence and near-optimal complexity in inner
proximal evaluations (upto a log factor) compared to VS-APM. But, unlike a
direct application of VS-APM, this scheme is characterized by larger
steplengths and better empirical behavior; (II) Convex. When $f(x,\omega)$ is
merely convex but smoothable, by suitable choices of the smoothing, steplength,
and batch-size sequences, smoothed VS-APM (or sVS-APM) produces sequences for
which expected sub-optimality diminishes at the rate of $\mathcal{O}(1/k)$ with
an optimal oracle complexity of $\mathcal{O}(1/\epsilon^2)$. Finally, sVS-APM
and VS-APM produce sequences that converge almost surely to a solution of the
original problem.
