Maximum cut

Maximum cut
A maximum cut.

For a graph, a maximum cut is a cut whose size is at least the size of any other cut. The problem of finding a maximum cut in a graph is known as the max-cut problem.

The problem can be stated simply as follows. One wants a subset S of the vertex set such that the number of edges between S and the complementary subset is as large as possible.

There is a more advanced version of the problem called weighted max-cut. In this version each edge has a real number, its weight, and the objective is to maximize not the number of edges but the total weight of the edges between S and its complement. The weighted max-cut problem is often, but not always, restricted to non-negative weights, because negative weights can change the nature of the problem.


Computational complexity

The following decision problem related to maximum cuts has been studied widely in theoretical computer science:

Given a graph G and an integer k, determine whether there is a cut of size at least k in G.

This problem is known to be NP-complete. It is easy to see that problem is in NP: a yes answer is easy to prove by presenting a large enough cut. The NP-completeness of the problem can be shown, for example, by a transformation from maximum 2-satisfiability (a restriction of the maximum satisfiability problem).[1] The weighted version of the decision problem was one of Karp's 21 NP-complete problems;[2] Karp showed the NP-completeness by a reduction from the partition problem.

The canonical optimization variant of the above decision problem is usually known as the maximum cut problem or max-cut problem and is defined as:

Given a graph G, find a maximum cut.

Polynomial-time algorithms

As the max-cut problem is NP-hard, no polynomial-time algorithms for max-cut in general graphs are known. However, a polynomial-time algorithm to find maximum cuts in planar graphs exists.[3]

Approximation algorithms

There is a simple randomized 0.5-approximation algorithm: for each vertex flip a coin to decide to which half of the partition to assign it.[4][5] In expectation, half of the edges are cut edges. This algorithm can be derandomized with the method of conditional probabilities; therefore there is a simple deterministic polynomial-time 0.5-approximation algorithm as well.[6][7] One such algorithm is: given a graph G = (V,E) start with an arbitrary partition of V and move a vertex from one side to the other if it improves the solution until no such vertex exists. The number of iterations is bound by O ( \left | E \right | ) because the algorithm improves the cut value by at least 1 at each step and the maximum cut is at most \left | E \right |. When the algorithm terminates, each vertex v \in V has at least half its edges in the cut (otherwise moving v to the other subset improves the solution). Therefore the cut is at least 0.5 \left | E \right |.

The best known max-cut algorithm is the 0.878…-approximation algorithm by Goemans and Williamson using semidefinite programming and randomized rounding.[8][9] It has been shown by Khot et al that this is the best possible approximation ratio for Max-Cut assuming the unique games conjecture.


The max-cut problem is APX-hard,[10] meaning that there is no polynomial-time approximation scheme (PTAS), arbitrarily close to the optimal solution, for it, unless P = NP. Moreover, it has been shown NP-hard to approximate the max-cut value to better than 16 / 17 = 0.941….[11][12]

Assuming the unique games conjecture (UGC), it is in fact NP-hard to approximate the max-cut value by a factor of \alpha_{GW} + \epsilon for any \epsilon > 0, where αGW = 0.878… is the approximation factor of Goemans–Williamson.[13] In other words, assuming the UGC and that BPP \neq NP, the Goemans–Williamson algorithm yields essentially the best polynomial-time-computable possible approximation ratio for the problem.

Maximum bipartite subgraph

A cut is a bipartite graph. The max-cut problem is essentially the same as the problem of finding a bipartite subgraph with the most edges.

Let G = (V,E) be a graph and let H = (V,X) be a bipartite subgraph of G. Then there is a partition (ST) of V such that each edge in X has one endpoint in S and another endpoint in T. Put otherwise, there is a cut in H such that the set of cut edges contains X. Therefore there is a cut in G such that the set of cut edges is a superset of X.

Conversely, let G = (V,E) be a graph and let X be a set of cut edges. Then H = (V,X) is a bipartite subgraph of H.

In summary, if there is a bipartite subgraph with k edges, there is a cut with at least k cut edges, and if there is a cut with k cut edges, there is a bipartite subgraph with k edges. Therefore the problem of finding a maximum bipartite subgraph is essentially the same as the problem of finding a maximum cut.[14] The same results on NP-hardness, inapproximability and approximability apply to both the maximum cut problem and the maximum bipartite subgraph problem.

See also



  • Ausiello, Giorgio; Crescenzi, Pierluigi; Gambosi, Giorgio; Kann, Viggo; Marchetti-Spaccamela, Alberto; Protasi, Marco (2003), Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties, Springer .
Maximum cut (optimisation version) is the problem ND14 in Appendix B (page 399).
Maximum cut (decision version) is the problem ND16 in Appendix A2.2.
Maximum bipartite subgraph (decision version) is the problem GT25 in Appendix A1.2.
  • Gaur, Daya Ram; Krishnamurti, Ramesh (2007), "LP rounding and extensions", in Gonzalez, Teofilo F., Handbook of Approximation Algorithms and Metaheuristics, Chapman & Hall/CRC .
  • Goemans, Michel X.; Williamson, David P. (1995), "Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming", Journal of the ACM 42 (6): 1115–1145, doi:10.1145/227683.227684 .
  • Hadlock, F. (1975), "Finding a Maximum Cut of a Planar Graph in Polynomial Time", SIAM J. Comput. 4 (3): 221–225, doi:10.1137/0204019 .
  • Håstad, Johan (2001), "Some optimal inapproximability results", Journal of the ACM 48 (4): 798–859, doi:10.1145/502090.502098 .
  • Karp, Richard M. (1972), "Reducibility among combinatorial problems", in Miller, R. E.; Thacher, J. W., Complexity of Computer Computation, Plenum Press, pp. 85–103 .
  • Khot, Subhash; Kindler, Guy; Mossel, Elchanan; O'Donnell, Ryan (2007), "Optimal inapproximability results for MAX-CUT and other 2-variable CSPs?", SIAM Journal on Computing 37 (1): 319–357, doi:10.1137/S0097539705447372 .
  • Khuller, Samir; Raghavachari, Balaji; Young, Neal E. (2007), "Greedy methods", in Gonzalez, Teofilo F., Handbook of Approximation Algorithms and Metaheuristics, Chapman & Hall/CRC .
  • Mitzenmacher, Michael; Upfal, Eli (2005), Probability and Computing: Randomized Algorithms and Probabilistic Analysis, Cambridge .
  • Motwani, Rajeev; Raghavan, Prabhakar (1995), Randomized Algorithms, Cambridge .
  • Newman, Alantha (2008), "Max cut", in Kao, Ming-Yang, Encyclopedia of Algorithms, Springer, pp. 1, doi:10.1007/978-0-387-30162-4_219 .
  • Papadimitriou, Christos H.; Yannakakis, Mihalis (1991), "Optimization, approximation, and complexity classes", Journal of Computer and System Sciences 43 (3): 425–440, doi:10.1016/0022-0000(91)90023-X .
  • Trevisan, Luca; Sorkin, Gregory; Sudan, Madhu; Williamson, David (2000), "Gadgets, Approximation, and Linear Programming", Proceedings of the 37th IEEE Symposium on Foundations of Computer Science: 617–626 .

Further reading

  • Barahona, Francisco; Grötschel, Martin; Jünger, Michael; Reinelt, Gerhard (1988), "An application of combinatorial optimization to statistical physics and circuit layout design", Operations Research 36 (3): 493–513, doi:10.1287/opre.36.3.493, JSTOR 170992 .

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