We study the problem of maximizing a monotone submodular function subject to a Multiple Knapsack constraint. The input is a set I of items, each has a non-negative weight, and a set of bins of arbitrary capacities. Also, we are given a submodular, monotone and non-negative function f over subsets of the items. The objective is to find a packing of a subset of items A \subseteq I in the bins such that f(A) is maximized. Our main result is an almost optimal polynomial time (1-e^{-1}- %DIFDELCMD < \eps%%%  )-approximation algorithm for the problem, for any %DIFDELCMD < \eps%%%  >0. The algorithm relies on a structuring technique which converts a general multiple knapsack constraint to a constraint in which the bins are partitioned into groups of exponentially increasing cardinalities, each consisting of bins of uniform capacity. We derive the result by combining structuring with a refined analysis of techniques for submodular optimization subject to knapsack constraints.
We study the problem of maximizing a monotone submodular function subject to a Multiple Knapsack constraint. The input is a set I of items, each has a non-negative weight, and a set of bins of arbitrary capacities. Also, we are given a submodular, monotone and non-negative function f over subsets of the items. The objective is to find a packing of a subset of items A \subseteq I in the bins such that f(A) is maximized. Our main result is an almost optimal polynomial time (1-e^{-1}- %DIFDELCMD < \eps%%% \varepsilon )-approximation algorithm for the problem, for any %DIFDELCMD < \eps%%% \varepsilon >0. The algorithm relies on a structuring technique which converts a general multiple knapsack constraint to a constraint in which the bins are partitioned into groups of exponentially increasing cardinalities, each consisting of bins of uniform capacity. We derive the result by combining structuring with a refined analysis of techniques for submodular optimization subject to knapsack constraints.
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