Connected max cut is polynomial for graphs without K_5 e as a minor

03/29/2019 ∙ by Brahim Chaourar, et al. ∙ 0

Given a graph G=(V, E), a connected cut δ (U) is the set of edges of E linking all vertices of U to all vertices of V U such that the induced subgraphs G[U] and G[V U] are connected. Given a positive weight function w defined on E, the connected maximum cut problem (CMAX CUT) is to find a connected cut Ω such that w(Ω) is maximum among all connected cuts. CMAX CUT is NP-hard even for planar graphs. In this paper, we prove that CMAX CUT is polynomial for graphs without K_5 e as a minor. We deduce a quadratic time algorithm for the minimum cut problem in the same class of graphs without computing the maximum flow.



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1 Introduction

We refer to Bondy and Murty [5] about graph theory terminolgy and facts.
Given an undirected graph and positive weights on the edges , the maximum (respectively, minimum) cut problem (MAX CUT, (respectively, MIN CUT)) is that of finding the set of vertices that maximizes (respectively, minimzes) the weight of the edges in the cut or or ; that is, the weight of the edges linking all vertices of to those of . The (decision variant of the) MAX CUT is one of the Karp’s original NP-complete problems [14], and has long been known to be NP-complete even if the problem is unweighted; that is, if for all [8]. This motivates the research to solve MAX CUT in special classes of graphs. MAX CUT problem is solvable in polynomial time for the following special classes of graphs: planar graphs [2, 11, 18], line graphs [10], graphs with bounded treewidth, or cographs [4]. But the problem remains NP-complete for chordal graphs, undirected path graphs, split graphs, tripartite graphs, graphs that are the complement of a bipartite graph [4] and planar graphs if the weights are of arbitrary sign [21]. Besides its theoretical importance, MAX CUT problem has applications in circuit layout design and statistical physics [1]. For a comprehensive survey of MAX CUT, the reader is referred to Poljak and Tuza [19] and Ben-Ameur et al. [3]. The best known algorithm for MAX CUT in planar graphs has running time complexity , where is the number of vertices of the given graph [20]. The main result of this paper is to exhibit a quadratic time algorithm for a special variant of MAX CUT in graphs without the excluded minor .
Let us give some definitions. Given an undirected graph and a subset of vertices , a connected cut is a cut where both induced subgraphs and are connected. Special connected cuts are trivial cuts, i.e., cuts with one single vertex in one side (when this vertex is not a disconnecting vertex). The corresponding weighted variant of MAX CUT for connected cuts is called connected maximum cut problem (CMAX CUT). It is clear that MAX CUT and CMAX CUT are the same problem for complete graphs. Since MAX CUT is NP-hard for complete graphs (see [14]) then CMAX CUT is also NP-hard in the general case. Another theoretical motivation is that CMAX CUT gives a lower bound for MAX CUT.
CMAX CUT has been proved NP-hard for planar graphs [12] and a linear time algorithm for series parallel graphs is presented in [7]. Some applications of CMAX CUT are: computing a market splitting for electricity markets [9, 15], forest planning problems [6], phylogenetics [16], image segmentation [22], and graph coloring [13].
Let and be two graphs with a vertex (respectively, an edge) of . The 1-sum (respectively, 2-sum) of and based on the vertices (respectively, edges ), , denoted or (respectively, or ), is the graph obtained by identifying and (respectively, and ) on a new vertex (respectively, edge ), and keeping (respectively, ), , as they are. Moreover, we can define the 2-sum for two subsets , , as the edge set of the 2-sum of their corresponding subgraphs , . Finally, for two classes , , such that , .
Let be the class of wheels (where ), the prism , , and , and be the class of graphs without as a minor. In this paper, we prove that CMAX CUT is polynomial (time) for this class of graphs. For the best of our knowledge, this is the largest known class of graphs for which CMAX CUT is polynomial. We have the following characterization of [23].

Theorem 1.1.

A graph if and only if is obtained by taking 1-sums and/or 2-sums of graphs of .

Given a positive rational and a class of graphs , we say that is -polynomial for CMAX CUT (respectively, MIN CUT) if there exists a polynomial algorithm with running time complexity which solves the considered problem for any graph , where . In this case, we say that such a graph is -polynomial for the considered problem. The class of all connected cuts of a given graph is denoted by . Moreover, for , the class of connected cuts of containing is denoted by .
We can see the hardness of CMAX CUT by enumeration through the following.

Proposition 1.2.

The remaining of the paper is organized as follows: in section 2, we prove that the class of graphs without the excluded minor is 2-polynomial for CMAX CUT and MIN CUT without computing the maximum flow for the latter problem, and we conclude in section 3.

2 is 2-polynomial for CMAX CUT and MIN CUT

First, we state the following lemma about the class of connected cuts when taking 2-sums.

Lemma 2.1.
  1. .

  2. .


(1) is trivial and (2) is direct because and . ∎

Now we start the process for proving the main result.

Lemma 2.2.

Let be a rational. Then:

  1. is -polynomial for CMAX CUT if and only if , are too.

  2. is -polynomial for CMAX CUT if and only if , are too.


It is not difficult to see that -polynomiality for CMAX CUT is preserved by minors. So if (respectively, ) is -polynomial for CMAX CUT then , are too. Now for the inverse way, we will see the two cases separately.
(1) According to the previous lemma, we need to solve two CMAX CUT problems, one in each , . So the whole running time complexity for solving CMAX CUT in is: , and we are done.
(2) Let , , and be a connected -maximum cut containing in , , among all connected cuts having the same property. It is not difficult to see that is a connected -maximum cut in . According to the previous lemma, we need to solve four CMAX CUT problems: in both , , and in both , , by changing the weight of to sum of all edges weights in order to force the corresponding solutions to contain this edge. Thus there exists an algorithm with running time complexity to solve CMAX CUT in , and we are done. ∎

Theorem 2.3.

is 2-polynomial for CMAX CUT.


Let and . It suffices to prove that because, in this case, we have to find the maximum weighted element from at most elements.
Case 1: If is then .
Case 2: If is then .
Case 3: If is ..
Case 4: Suppose now that is . We will prove that . Let be the outside cycle of , be the class of simple paths of with vertices and , and . Now let be the application defined from to such that . It is not difficult to see that is a bijection. In the other hand, and if . Thus , and we are done. ∎

Now we can state our main result.

Corollary 2.4.

is 2-polynomial for CMAX CUT.


Direct from Theorem 1.1, Lemma 2.2, and Theorem 2.3. ∎

We have a similar result for MIN CUT by using the following lemma [7].

Lemma 2.5.

Given a connected graph and a positive weight function defined on . Then any -minimum cut is a connected cut of .

And we can state a version of Corollary 2.4 for MIN CUT.

Corollary 2.6.

There exists a quadratic time algorithm for solving MIN CUT in without computing the maximum flow.


Direct from Lemma 2.5 and by adapting the quadratic algorithm of CMAX CUT. ∎

Note that, according to Proposition 1.2, . Thus we can get a larger class of graphs by taking 2-sums of and copies of for which CMAX CUT and MIN CUT have quadratic running time complexity.
In the other hand, by using Lemma 2.2 and similar decomposition theorems as for Theorem 1.1, we get linear time algorithms for CMAX CUT and MIN CUT (without computing the maximum flow) in large classes of graphs.
Finally, we can have a quadratic running time complexity for the famous Hamitonian Cycle Problem (HC) in the following class of graphs.

Corollary 2.7.

HC has quadratic running time complexity in graphs without the two excluded minors and .


Since the considered class of graphs is a subclass of both planar graphs and , then deciding if a given graph from this class contains a Hamiltonian cycle is equivalent to decide if a connected maximum cardinality cut (i.e., CMAX CUT with weights for any edge ) of the dual graph has cardinality . ∎

This result is interesting because HC is NP-complete in maximal planar graphs [17].

3 Conclusion

We have proved that CMAX CUT and MIN CUT have quadratic running time complexity for graphs with the excluded minor . Further directions are improving this running time complexity and studying CMAX CUT in larger classes of graphs than .


  • [1] F. Barahona, M. Grötschel, M. Jünger, and G. Reinelt (1988),

    An application of combinatorial optimization to statistical physics and circuit layout design

    , Operations Research 36: 493-513.
  • [2] F. Barahona, (1990), Planar multicommodity flows, max cut, and the Chinese postman problem, in: Polyhedral Combinatorics, Proceedings DIMACS Workshop, Morristown, New Jersey, 1989, W. Cook, P.D. Seymour (eds.) [DIMACS Series in Discrete Mathematics and Theoretical Computer Science, Volume 1], American Mathematical Society, Providence, Rhode Island: 189-202.
  • [3] W. Ben-Ameur, A. R. Mahjoub, and J. Neto, The maximum cut problem, in: Paradigms of Combinatorial Optimization, Problems and New Approaches, 2nd edition, J. Wiley and Sons, USA, V. T. Paschos (ed.), 2014.
  • [4] H. L. Bodlaender and K. Jansen (2000), On the complexity of the maximum cut problem, Nordic Journal of Computing 7(1): 14-31.
  • [5] J. A. Bondy and U. S. R. Murty (2008), Graph Theory with Applications, Elsevier, New York.
  • [6] R. Carvajal, M. Constantino, M. Goycoolea, J. P. Vielma, and A. Weintraub (2013), Imposing connectivity constraints in forest planning, Operations Research 61 (4): 824–836.
  • [7] Chaourar, B., A linear time algorithm for a variant of max cut in series parallel graphs, Advances in Operations Research 35 (1D), 29-35, 2010.
  • [8] M. R. Garey, D. S. Johnson, and L. Stockmeyer (1976), Some simplified NP-complete graph problems, Theoretical Computer Science 1: 237-267.
  • [9] V. Grimm, T. Kleinert, F. Liers, M. Schmidt, and G. Zöttl (2019), Optimal price zones of electricity markets: a mixed-integer multilevel model and global solution approaches, Optimization Methods and Software 34 (2): 406-436.
  • [10] V. Guruswami (1999), Maximum cut on line and total graphs, Discrete Applied Mathematics 92 (2-3): 217-221.
  • [11] F. Hadlock (1975), Finding a maximum cut of a planar graph in polynomial time, SIAM Journal on Computing 4: 221-225.
  • [12] D. J. Haglin and S. M. Venkatesan (1991), Approximation and intractability results for the maximum cut problem and its variants, IEEE Transactions on Computers 40 (1): 110-113.
  • [13] . C. Hojny and M. E. Pfetsch (2018), Polytopes associated with symmetry handling, Mathematical Programming.
  • [14] R. M. Karp (1972), Reducibility among combinatorial problems, in: Complexity of Computer Computations, Miller and Thatcher, Plenum Press: 85-104.
  • [15] T. Kleinert and M. Schmidt (2018), Global optimization of multilevel electricity market models including network design and graph partitioning, Technical Report, FAU Erlangen-Nürnberg.
  • [16] F. Liers, A. Martin, and S. Pape (2016), Binary Steiner trees: structural results and an exact solution approach, Discrete Optimization 21: 85-117.
  • [17] T. Nishizeki, T. Asano, and T. Watanabe (1983), An approximation algorithm for the Hamiltonian walk problem on maximal planar graphs, Discrete Applied Mathematics 5 (2): 211-222.
  • [18] G. I. Orlova and Y. G. Dorfman (1972), Finding the maximal cut in a graph, Engineering Cybernetics 10 (3): 502-506.
  • [19] Poljak and Tuza (1995), The max-cut problem – a survey, in: Special Year on Combinatorial Optimization, W. Cook, L. Lovasz and P. Seymour (eds.), DIMACS series in Discrete Mathematics and Theoretical Computer Science, American Mathematical Society, 1995.
  • [20] W.-K. Shih, S. Wu, and Y. S. Kuo (1990), Unifying maximum cut and minimum cut of a planar graph, IEEE Transactions on Computers 39 (5): 694–697.
  • [21] A. P. Terebenkov (1991), NP-completeness of maximum-cut and cycle-covering problems for a planar graph, Cybernetics and Systems Analysis 27 (1): 16-20.
  • [22] S. Vicente, V. Kolmogorov, and C. Rother (2008), Graph cut based image segmentation with connectivity priors

    , Computer vision and pattern recognition CVPR 2008 (IEEE conference on CVPR): 1-8.

  • [23] K. Wagner (1960), Bemerkungen zu Hadwigers Vermutung, Mathematische Annalen 141: 433-451.