Set packing

The maximum set packing problem can be formulated as the following integer linear program.

maximize	${\displaystyle \sum _{S\in {\mathcal {S}}}x_{S}}$		(maximize the total number of subsets)
subject to	${\displaystyle \sum _{S\colon e\in S}x_{S}\leqslant 1}$	for all ${\displaystyle e\in {\mathcal {U}}}$	(selected sets have to be pairwise disjoint)
	${\displaystyle x_{S}\in \{0,1\}}$	for all ${\displaystyle S\in {\mathcal {S}}}$.	(every set is either in the set packing or not)

The set packing problem is not only NP-complete, but its optimization version (general maximum set packing problem) has been proven as difficult to approximate as the maximum clique problem; in particular, it cannot be approximated within any constant factor.[1] The best known algorithm approximates it within a factor of ${\displaystyle O({\sqrt {|U|}})}$.[2] The weighted variant can also be approximated as well.[3]

However, the problem does have a variant which is more tractable: if we assume no subset exceeds k≥3 elements, the answer can be approximated within a factor of k/2 + ε for any ε > 0; in particular, the problem with 3-element sets can be approximated within about 50%. In another more tractable variant, if no element occurs in more than k of the subsets, the answer can be approximated within a factor of k. This is also true for the weighted version.

There is a one-to-one polynomial-time reduction between the independent set problem and the set packing problem:

• Given a set packing problem on a collection ${\displaystyle {\mathcal {S}}}$, create a graph where for each set ${\displaystyle S\in {\mathcal {S}}}$ there is a vertex ${\displaystyle v_{S}}$, and there is an edge between ${\displaystyle v_{S}}$ and ${\displaystyle v_{T}}$ if ${\displaystyle S\cap T\neq \varnothing }$. Now every independent set of vertices in the generated graph corresponds to a set packing in ${\displaystyle {\mathcal {S}}}$.
• Given an independent vertex set problem on a graph ${\displaystyle G(V,E)}$, create a collection of sets where for each vertex ${\displaystyle v}$ there is a set ${\displaystyle S_{v}}$ containing all edges adjacent to ${\displaystyle v}$. Now every set packing in the generated collection corresponds to an independent vertex set in ${\displaystyle G(V,E)}$.

This is also a bidirectional PTAS reduction, and it shows that the two problems are equally difficult to approximate.

Matching and 3-dimensional matching are special cases of set packing. A maximum-size matching can be found in polynomial time, but finding a largest 3-dimensional matching or a largest independent set is NP-hard.

Set packing is one among a family of problems related to covering or partitioning the elements of a set. One closely related problem is the set cover problem. Here, we are also given a set S and a list of sets, but the goal is to determine whether we can choose k sets that together contain every element of S. These sets may overlap. The optimization version finds the minimum number of such sets. The maximum set packing need not cover every possible element.

The NP-complete exact cover problem, on the other hand, requires every element to be contained in exactly one of the subsets. Finding such an exact cover at all, regardless of size, is an NP-complete problem. However, if we create a singleton set for each element of S and add these to the list, the resulting problem is about as easy as set packing.

Karp originally showed set packing NP-complete via a reduction from the clique problem.

See also: Packing in a hypergraph.

• Maximum Set Packing, Viggo Kann.
• "set packing". Dictionary of Algorithms and Data Structures, editor Paul E. Black, National Institute of Standards and Technology. Note that the definition here is somewhat different.
• Steven S. Skiena. "Set Packing". The Algorithm Design Manual.
• Pierluigi Crescenzi, Viggo Kann, Magnús Halldórsson, Marek Karpinski and Gerhard Woeginger. "Maximum Set Packing". A compendium of NP optimization problems. Last modified March 20, 2000.
• Michael R. Garey and David S. Johnson (1979). Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman. ISBN 978-0-7167-1045-5. A3.1: SP3, pg.221.
• Vazirani, Vijay V. (2001). Approximation Algorithms. Springer-Verlag. ISBN 978-3-540-65367-7.

• : A Pascal program for solving the problem. From Discrete Optimization Algorithms with Pascal Programs by MacIej M. Syslo, ISBN 0-13-215509-5.
• Benchmarks with Hidden Optimum Solutions for Set Covering, Set Packing and Winner Determination
• Solving packaging problem in PHP
• Optimizing Three-Dimensional Bin Packing