  In this study, we consider the subset selection problems with submodular or
monotone discrete objective functions under partition matroid constraints where
the thresholds are dynamic. We focus on POMC, a simple Pareto optimization
approach that has been shown to be effective on such problems. Our analysis
departs from singular constraint problems and extends to problems of multiple
constraints. We show that previous results of POMC's performance also hold for
multiple constraints. Our experimental investigations on random undirected
maxcut problems demonstrate POMC's competitiveness against the classical GREEDY
algorithm with restart strategy.
