  We consider clustering problems with {\em non-uniform lower bounds and
outliers}, and obtain the {\em first approximation guarantees} for these
problems. We have a set $\F$ of facilities with lower bounds $\{L_i\}_{i\in\F}$
and a set $\D$ of clients located in a common metric space
$\{c(i,j)\}_{i,j\in\F\cup\D}$, and bounds $k$, $m$. A feasible solution is a
pair $\bigl(S\sse\F,\sigma:\D\mapsto S\cup\{\mathsf{out}\}\bigr)$, where
$\sigma$ specifies the client assignments, such that $|S|\leq k$,
$|\sigma^{-1}(i)|\geq L_i$ for all $i\in S$, and
$|\sigma^{-1}(\mathsf{out})|\leq m$. In the {\em lower-bounded min-sum-of-radii
with outliers} (\lbksro) problem, the objective is to minimize $\sum_{i\in
S}\max_{j\in\sigma^{-1}(i)}c(i,j)$, and in the {\em lower-bounded $k$-supplier
with outliers} (\lbkso) problem, the objective is to minimize $\max_{i\in
S}\max_{j\in\sigma^{-1}(i)}c(i,j)$.
  We obtain an approximation factor of $12.365$ for \lbksro, which improves to
$3.83$ for the non-outlier version (i.e., $m=0$). These also constitute the
{\em first} approximation bounds for the min-sum-of-radii objective when we
consider lower bounds and outliers {\em separately}. We apply the primal-dual
method to the relaxation where we Lagrangify the $|S|\leq k$ constraint. The
chief technical contribution and novelty of our algorithm is that, departing
from the standard paradigm used for such constrained problems, we obtain an
$O(1)$-approximation {\em despite the fact that we do not obtain a
Lagrangian-multiplier-preserving algorithm for the Lagrangian relaxation}. We
believe that our ideas have {broader applicability to other clustering problems
with outliers as well.}
  We obtain approximation factors of $5$ and $3$ respectively for \lbkso and
its non-outlier version. These are the {\em first} approximation results for
$k$-supplier with {\em non-uniform} lower bounds.
