$\ell_1$-sparsity Approximation Bounds for Packing Integer Programs

We consider approximation algorithms for packing integer programs (PIPs) of the form $\max{\langle c, x\rangle : Ax \le b, x \in {0,1}^n}$ where $c$, $A$, and $b$ are nonnegative. We let $W = \min{i,j} bi / A{i,j}$ denote the width of $A$ which is at least $1$. Previous work by Bansal et al. \cite{bansal-sparse} obtained an $\Omega(\frac{1}{\Delta0^{1/\lfloor W \rfloor}})$-approximation ratio where $\Delta0$ is the maximum number of nonzeroes in any column of $A$ (in other words the $\ell0$-column sparsity of $A$). They raised the question of obtaining approximation ratios based on the $\ell1$-column sparsity of $A$ (denoted by $\Delta1$) which can be much smaller than $\Delta0$. Motivated by recent work on covering integer programs (CIPs) \cite{cq,chs-16} we show that simple algorithms based on randomized rounding followed by alteration, similar to those of Bansal et al. \cite{bansal-sparse} (but with a twist), yield approximation ratios for PIPs based on $\Delta1$. First, following an integrality gap example from \cite{bansal-sparse}, we observe that the case of $W=1$ is as hard as maximum independent set even when $\Delta1 \le 2$. In sharp contrast to this negative result, as soon as width is strictly larger than one, we obtain positive results via the natural LP relaxation. For PIPs with width $W = 1 + \epsilon$ where $\epsilon \in (0,1]$, we obtain an $\Omega(\epsilon^2/\Delta1)$-approximation. In the large width regime, when $W \ge 2$, we obtain an $\Omega((\frac{1}{1 + \Delta1/W})^{{1/(W-1)})$-approximation.} We also obtain a $(1-\epsilon)$-approximation when $W = \Omega(\frac{\log (\Delta1/\epsilon)}{\epsilon^2})$.