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\(\ell _1\)-Sparsity approximation bounds for packing integer programs

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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 A, b and c are nonnegative. We let \(W = \min _{i,j} b_i / A_{i,j}\) denote the width of A which is at least 1. Previous work by Bansal et al. (Theory Comput 8(24):533–565, 2012) obtained an \(\varOmega (\frac{1}{\varDelta _0^{1/\lfloor W \rfloor }})\)-approximation ratio where \(\varDelta _0\) is the maximum number of nonzeroes in any column of A (in other words the \(\ell _0\)-column sparsity of A). They raised the question of obtaining approximation ratios based on the \(\ell _1\)-column sparsity of A (denoted by \(\varDelta _1\)) which can be much smaller than \(\varDelta _0\). Motivated by recent work on covering integer programs (Chekuri and Quanrud, in: Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms, pp 1596–1615. SIAM, 2019; Chen et al., in: Proceedings of the Twenty-seventh Annual ACM-SIAM Symposium on Discrete Algorithms, pp 1984–2003. SIAM, 2016) we show that simple algorithms based on randomized rounding followed by alteration, similar to those of Bansal et al. (Theory Comput 8(24):533–565, 2012) (but with a twist), yield approximation ratios for PIPs based on \(\varDelta _1\). First, following an integrality gap example from (Theory Comput 8(24):533–565, 2012), we observe that the case of \(W=1\) is as hard as maximum independent set even when \(\varDelta _1 \le 2\). 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 \(\varOmega (\epsilon ^2/\varDelta _1)\)-approximation. In the large width regime, when \(W \ge 2\), we obtain an \(\varOmega ((\frac{1}{1 + \varDelta _1/W})^{1/(W-1)})\)-approximation. We also obtain a \((1-\epsilon )\)-approximation when \(W = \varOmega (\frac{\log (\varDelta _1/\epsilon )}{\epsilon ^2})\). Viewing the rounding algorithms as contention resolution schemes, we obtain approximation algorithms in the more general setting when the objective is a non-negative submodular function.

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  1. 1.

    We can allow the variables to have general integer upper bounds instead of restricting them to be boolean. As observed in [1], one can reduce this more general case to the \(\{0,1\}\) case without too much loss in the approximation.

  2. 2.

    We say a family of subsets \({\mathcal {I}}\subseteq 2^N\) is downward closed if for all \(A \subseteq B \subseteq N\), if \(B \in {\mathcal {I}}\), then \(A \in {\mathcal {I}}\).

  3. 3.

    For non-negative functions there have been subsequent improvements in the approximation ratio [2, 10] but the dependence on \(\alpha \) is unclear and since the precise approximation ratios are not the focus in this paper, we confine ourselves to the simpler algorithm and bound from [11].

  4. 4.

    For \(x \in [0,1]^n\) we use \({\text {support}}(x)\) to denote the set \(\{i \mid x_i > 0\}\).


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Correspondence to Manuel R. Torres.

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C. Chekuri and K. Quanrud supported in part by NSF Grant CCF-1526799. M. Torres supported in part by fellowships from NSF and the Sloan Foundation. A preliminary version of this work appeared in the proceedings of the 20th conference on Integer Programming and Combinatorial Optimization (IPCO 2019)

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Chekuri, C., Quanrud, K. & Torres, M.R. \(\ell _1\)-Sparsity approximation bounds for packing integer programs. Math. Program. (2020).

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  • Approximation algorithms
  • Sparse packing integer programs
  • Randomized rounding
  • Submodular optimization

Mathematics Subject Classification

  • Primary 68W25
  • Secondary 90C59