Skip to main content

Advertisement

Log in

The Feedback Arc Set Problem with Triangle Inequality Is a Vertex Cover Problem

  • Published:
Algorithmica Aims and scope Submit manuscript

Abstract

We consider the (precedence constrained) Minimum Feedback Arc Set problem with triangle inequalities on the weights, which finds important applications in problems of ranking with inconsistent information. We present a surprising structural insight showing that the problem is a special case of the minimum vertex cover in hypergraphs with edges of size at most 3.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. Recall that a 0∖1 solution δ is minimal if the removal of any arc (i,j) from its support makes it unfeasible.

  2. When (i,j′)∈M v this is the only possible case, namely case (ii) does not hold. Indeed, see Fig. 6, when (i,j′)∈M v , the following is a valid constraint δ v,j+δ j,k+δ k,v ≥1 and we are considering the case with δ v,j=δ j,k=0.

References

  1. Aharoni, R., Holzman, R., Krivelevich, M.: On a theorem of Lovász on covers in τ-partite hypergraphs. Combinatorica 16(2), 149–174 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  2. Ailon, N.: Aggregation of partial rankings, p-ratings and top-m lists. Algorithmica 57(2), 284–300 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  3. Ailon, N., Avigdor-Elgrabli, N., Liberty, E., van Zuylen, A.: Improved approximation algorithms for bipartite correlation clustering. SIAM J. Comput. 41(5), 1110–1121 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  4. Ailon, N., Charikar, M., Newman, A.: Aggregating inconsistent information: Ranking and clustering. J. ACM 55(5) (2008)

  5. Ambühl, C., Mastrolilli, M.: Single machine precedence constrained scheduling is a vertex cover problem. Algorithmica 53(4), 488–503 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  6. Ambühl, C., Mastrolilli, M., Mutsanas, N., Svensson, O.: Precedence constraint scheduling and connections to dimension theory of partial orders. Bull. Eur. Assoc. Theor. Comput. Sci. 95, 45–58 (2008)

    Google Scholar 

  7. Ambühl, C., Mastrolilli, M., Mutsanas, N., Svensson, O.: On the approximability of single-machine scheduling with precedence constraints. Math. Oper. Res. 36(4), 653–669 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  8. Ambühl, C., Mastrolilli, M., Svensson, O.: Inapproximability results for sparsest cut, optimal linear arrangement, and precedence constraint scheduling. In: Proceedings of FOCS, pp. 329–337 (2007)

    Google Scholar 

  9. Bansal, N., Khot, S.: Optimal Long-Code test with one free bit. In: Proceedings of FOCS, pp. 453–462 (2009)

    Google Scholar 

  10. Correa, J.R., Schulz, A.S.: Single machine scheduling with precedence constraints. Math. Oper. Res. 30(4), 1005–1021 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  11. Even, G., Naor, J., Rao, S., Schieber, B.: Divide-and-conquer approximation algorithms via spreading metrics. J. ACM 47(4), 585–616 (2000)

    Article  MathSciNet  Google Scholar 

  12. Even, G., Naor, J., Schieber, B., Sudan, M.: Approximating minimum feedback sets and multicuts in directed graphs. Algorithmica 20(2), 151–174 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  13. Grötschel, M., Jünger, M., Reinelt, G.: Acyclic subdigraphs and linear orderings: polytopes, facets, and a cutting plane algorithm. In: Graphs and Orders, pp. 217–264 (1985)

    Chapter  Google Scholar 

  14. Guruswami, V., Håstad, J., Manokaran, R., Raghavendra, P., Charikar, M.: Beating the random ordering is hard: every ordering csp is approximation resistant. SIAM J. Comput. 40(3), 878–914 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  15. Hall, L.A., Schulz, A.S., Shmoys, D.B., Wein, J.: Scheduling to minimize average completion time: off-line and on-line algorithms. Math. Oper. Res. 22, 513–544 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  16. Kann, V.: On the Approximability of NP-Complete Optimization Problems. Ph.D. thesis, Department of Numerical Analysis and Computing Science, Royal Institute of Technology, Stockholm (1992)

  17. Karp, R.: Reducibility Among Combinatorial Problems, pp. 85–103. Plenum, New York (1972)

    Google Scholar 

  18. Kenyon-Mathieu, C., Schudy, W.: How to rank with few errors. In: Proceedings of STOC, pp. 95–103 (2007)

    Google Scholar 

  19. Krivelevich, M.: Approximate set covering in uniform hypergraphs. J. Algorithms 25(1), 118–143 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  20. Kuhn, F., Mastrolilli, M.: Vertex cover in graphs with locally few colors. Inf. Comput. 222, 265–277 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  21. Lawler, E.L., Lenstra, J.K., Rinnooy Kan, A.H.G., Shmoys, D.B.: Sequencing and scheduling: Algorithms and complexity. In: Graves, S.C., Rinnooy Kan, A.H.G., Zipkin, P. (eds.) Handbooks in Operations Research and Management Science, vol. 4, pp. 445–552. North-Holland, Amsterdam (1993)

    Google Scholar 

  22. Lempel, A., Cederbaum, I.: Minimum feedback arc and vertex sets of a directed graph. IEEE Trans. Circuit Theory 4(13), 399–403 (1966)

    Article  MathSciNet  Google Scholar 

  23. Pardalos, P., Du, D.: Handbook of Combinatorial Optimization: Supplement vol. 1. Springer, Berlin (1999)

    Google Scholar 

  24. Schuurman, P., Woeginger, G.J.: Polynomial time approximation algorithms for machine scheduling: ten open problems. J. Sched. 2(5), 203–213 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  25. Seymour, P.D.: Packing directed circuits fractionally. Combinatorica 15(2), 281–288 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  26. Trotter, W.T.: Combinatorics and Partially Ordered Sets: Dimension Theory. Johns Hopkins Series in the Mathematical Sciences. Johns Hopkins University Press, Baltimore (1992)

    MATH  Google Scholar 

  27. van Zuylen, A., Hegde, R., Jain, K., Williamson, D.P.: Deterministic pivoting algorithms for constrained ranking and clustering problems. In: Proceedings of SODA, pp. 405–414 (2007)

    Google Scholar 

  28. van Zuylen, A., Williamson, D.P.: Deterministic pivoting algorithms for constrained ranking and clustering problems. Math. Oper. Res. 34(3), 594–620 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  29. Vazirani, V.V.: Approximation Algorithms. Springer, Berlin (2001)

    Google Scholar 

Download references

Acknowledgements

To my beloved mom Elsa.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Monaldo Mastrolilli.

Additional information

This research is supported by Swiss National Science Foundation project N. 200020-144491/1 “Approximation Algorithms for Machine Scheduling Through Theory and Experiments” and by Hasler Foundation Grant 11099.

Appendices

Appendix A: Ranking with Probability Inequalities: a Counterexample

The following example shows that probabilities inequalities are not sufficient for (3a)–(3d) to be a proper formulation:

$$w_{(i,j)}+w_{(j,i)}=1\quad \mbox{for all distinct } i,j $$

Consider the instance with 8 nodes with weight zero on the arcs displayed in Fig. 7 (therefore the reversed arcs have weight 1). Moreover, all the arcs in {2,3}×{7,8} have weight 1 (the reversed zero). Finally, all the remaining arcs have weight 0.5, namely those in {1}×{4,5,6} and the reversed ones. A feasible solution for (2a)–(2d) is obtained by picking all the displayed arcs in Fig. 7 and none of the reversed ones (therefore we have to pick also those in {2,3}×{7,8}, {7,8}×{2,3}, {4,5,6}×{1} and {1}×{4,5,6} in order to satisfy the constraints in (2a)–(2d)). This solution has value 7, whereas any total ordering has value not smaller than 7.5 (the best total ordering is (2,3,4,5,6,7,8,1)).

Fig. 7
figure 7

Counterexample for probability inequalities

Appendix B: A Comment on Formulation (3a)–(3d)

If the poset is not empty the additional constraints that are present in formulation (3a)–(3d) but not in (2a)–(2d) are also necessary. Indeed, in Figure 8 any permutation that complies with the precedence constraints has value larger than the solution suggested in the picture with a cycle.

Fig. 8
figure 8

Solution \(\delta^{*}_{(1,2)}= \delta^{*}_{(2,3)}=\delta^{*}_{(3,4)}= \delta^{*}_{(4,1)}=\delta^{*}_{(1,3)}= \delta^{*}_{(3,1)}=\delta^{*}_{(2,4)}= \delta^{*}_{(4,2)}=1\) has value smaller than any valid permutation

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mastrolilli, M. The Feedback Arc Set Problem with Triangle Inequality Is a Vertex Cover Problem. Algorithmica 70, 326–339 (2014). https://doi.org/10.1007/s00453-013-9811-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00453-013-9811-2

Keywords

Navigation