Technical Report MSC-2011-08

TR#:MSC-2011-08
Class:MSC
Title: Submodular and Linear Maximization with Knapsack Constraints
Authors: Ariel Kulik
Supervisors: Hadas Shachnai
PDFMSC-2011-08.pdf
Abstract: Submodular maximization generalizes many fundamental problems in discrete optimization, including Max-Cut in directed/undirected graphs, maximum coverage, maximum facility location and marketing over social networks.

In this work we consider the problem of maximizing any submodular function subject to $d$ knapsack constraints, where $d$ is a fixed constant. For short, we call this problem $\SUB$. We establish a strong relation between the discrete problem and its continuous relaxation, obtained through {\em extension by expectation} of the submodular function. Formally, we show that, for any non-negative submodular function, an $\alpha$-approximation algorithm for the continuous relaxation implies a randomized $(\alpha - \eps)$-approximation algorithm for $\SUB$. We use this relation to improve the best known approximation ratio for the problem to $1/4- \eps$, for any $\eps > 0$, and to obtain a nearly optimal $(1-e^{-1}-\eps)-$approximation ratio for the monotone case, for any $\eps>0$. We further show that the probabilistic domain defined by a continuous solution can be reduced to yield a polynomial size domain, given an oracle for the extension by expectation. This leads to a deterministic version of our technique.

Our approach has a potential of wider applicability, which we demonstrate on the examples of the Generalized Assignment Problem and Maximum Coverage with additional knapsack constraints.

We also consider the special case of $\SUB$ in which the objective function is {\em linear}. In this case, our problem reduces to the classic {\em d-dimensional knapsack} problem. It is known that, unless $P=NP$, there is no {\em fully polynomial time approximation scheme} for $d$-dimensional knapsack, already for $d=2$. The best known result is a {\em polynomial time approximation scheme (PTAS)} due to Frieze and Clarke (\textit{European J. of Operational Research, 100--109, 1984}) for the case where $d \geq 2$ is some fixed constant. A fundamental open question is whether the problem admits an {\em efficient PTAS (EPTAS)}.

We resolve this question by showing that there is no EPTAS for $d$-dimensional knapsack, already for $d=2$, unless $W[1]=FPT$. Furthermore, we show that unless all problems in SNP are solvable in sub-exponential time, there is no approximation scheme for two-dimensional knapsack whose running time is $f(1/\eps) |\cI|^{o(\sqrt{1/\eps})}$, for any function $f$. Together, the two results suggest that a significant improvement over the running time of the scheme of Frieze and Clarke is unlikely to exist.

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