  Pruhs and Woeginger prove the existence of FPTAS's for a general class of
minimization and maximization subset selection problems. Without losing
generality from the original framework, we prove how better asymptotic
worst-case running times can be achieved if a $\rho$-approximation algorithm is
available, and in particular we obtain matching running times between
maximization and minimization subset selection problems. We directly apply this
result to the Minimum Knapsack Problem, for which the original framework yields
an FPTAS with running time $O(n^5/\epsilon)$, where $\epsilon$ is the required
accuracy and $n$ is the number of items, and obtain an FPTAS with running time
$O(n^3/\epsilon)$, thus improving the running time by a quadratic factor in the
worst case.
