# Feedback arc set

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This directed graph has no cycles: it is not possible to get from any vertex (point) back to that same point, following the connections in the direction indicated by the arrows.

In graph theory, a directed graph may contain directed cycles, a one-way loop of edges. In some applications, such cycles are undesirable, and we wish to eliminate them and obtain a directed acyclic graph (DAG). One way to do this is simply to drop edges from the graph to break the cycles. A feedback arc set (FAS) or feedback edge set is a set of edges which, when removed from the graph, leave a DAG. Put another way, it's a set containing at least one edge of every cycle in the graph.

Closely related are the feedback vertex set, which is a set of vertices containing at least one vertex from every cycle in the directed graph, and the minimum spanning tree, which is the undirected variant of the feedback arc set problem.

A minimal feedback arc set (one that can not be reduced in size by removing any edges) has the additional property that, if the edges in it are reversed rather than removed, then the graph remains acyclic. Finding a small edge set with this property is a key step in layered graph drawing.[1][2]

Sometimes it is desirable to drop as few edges as possible, obtaining a minimum feedback arc set (MFAS), or dually a maximum acyclic subgraph. This is a hard computational problem, for which several approximate solutions have been devised.

## Example

As a simple example, consider the following hypothetical situation, where in order to achieve something, certain things must be achieved before other things:

• Your goal is to get the lawnmower.
• George says he will give you a piano, but only in exchange for a lawnmower.
• Harry says he will give you a lawnmower, but only in exchange for a microwave.
• Jane says she will give you a microwave, but only in exchange for a piano.

We can express this as a graph problem. Let each vertex represent an item, and add an edge from A to B if you must have A to obtain B. Unfortunately, you don't have any of the three items, and because this graph is cyclic, you can't get any of them either.

However, suppose you offer George \$100 for his piano. If he accepts, this effectively removes the edge from the lawnmower to the piano, because you no longer need the lawnmower to get the piano. Consequently, the cycle is broken, and you can trade twice to get the lawnmower. This one edge constitutes a feedback arc set.

## Minimum feedback arc set

As in the above example, there is usually some cost associated with removing an edge. For this reason, we'd like to remove as few edges as possible. Removing one edge suffices in a simple cycle, but in general figuring out the minimum number of edges to remove is an NP-hard problem called the minimum feedback arc set or maximum acyclic subgraph problem.

### Theoretical results

This problem is particularly difficult in k-edge-connected graphs for large k, where each edge falls in many different cycles. The decision version of the problem, which is NP-complete, asks whether all cycles can be broken by removing at most k edges; this was one of Richard M. Karp's 21 NP-complete problems, shown by reducing from the vertex cover problem.[3][4]

Although NP-complete, the feedback arc set problem is fixed-parameter tractable: there exists an algorithm for solving it whose running time is a fixed polynomial in the size of the input graph (independent of the number of edges in the set) but exponential in the number of edges in the feedback arc set.[5] Alternatively, a fixed-parameter tractable algorithm is given by a dynamic programming technique that depends only exponentially on the dimension of the cycle space of the graph.[6]

The minimum feedback arc set problem is APX-hard, which means that (assuming P ≠ NP) there is a hard limit on its approximation quality, a constant c > 1 such that every polynomial-time approximation algorithm will sometimes return an edge set larger than c times the optimal size. The proof involves approximation-preserving reductions from vertex cover to feedback vertex set, and from feedback vertex set to feedback arc set.[7][8][9] More specifically, because vertex cover has no approximation better than 1.3606 unless P ≠ NP, the same is true for feedback arc set. That is, it is possible to take c = 1.3606.[10] If the unique games conjecture is true, this inapproximability threshold could be increased to arbitrarily close to 2.[11]

On the other hand, the best known approximation algorithm has the non-constant approximation ratio O(log n log log n).[9][12] For the dual problem, of approximating the maximum number of edges in an acyclic subgraph, an approximation somewhat better than 1/2 is possible.[13][14] Determining whether feedback arc set has a constant-ratio approximation algorithm, or whether a non-constant ratio is necessary, remains an open problem.

 Unsolved problem in mathematics:Does the feedback arc set problem have an approximation algorithm with a constant approximation ratio?(more unsolved problems in mathematics)

If the input digraphs are restricted to be tournaments, the resulting problem is known as the minimum feedback arc set problem on tournaments (FAST). This restricted problem does admit a polynomial-time approximation scheme, and this still holds for a restricted weighted version of the problem.[15] A subexponential fixed parameter algorithm for the weighted FAST was given by Karpinski & Schudy (2010).[16]

On the other hand, if the edges are undirected, the problem of deleting edges to make the graph cycle-free is equivalent to finding a minimum spanning tree, which can be done easily in polynomial time.

### Approximations

Several approximation algorithms for the problem have been developed[17] - including Monte Carlo randomized algorithm that solves the problem in polynomial time with arbitrary probability.[18] A particularly simple algorithm is the following:

• Fix an arbitrary permutation of the vertices; that is, label the vertices from 1 through n, choosing arbitrarily which vertex to give which label.
• Construct two subgraphs L and R, containing the edges (u, v) where u < v, and those where u > v, respectively.

Now both L and R are acyclic subgraphs of G, and at least one of them is at least half the size of the maximum acyclic subgraph.[19]:348

## References

1. ^ Di Battista, Giuseppe; Eades, Peter; Tamassia, Roberto; Tollis, Ioannis G. (1998), "Layered Drawings of Digraphs", Graph Drawing: Algorithms for the Visualization of Graphs, Prentice Hall, pp. 265–302, ISBN 9780133016154.
2. ^ Bastert, Oliver; Matuszewski, Christian (2001), "Layered drawings of digraphs", in Kaufmann, Michael; Wagner, Dorothea (eds.), Drawing Graphs: Methods and Models, Lecture Notes in Computer Science, 2025, Springer-Verlag, pp. 87–120, doi:10.1007/3-540-44969-8_5.
3. ^ Karp, Richard M. (1972), "Reducibility Among Combinatorial Problems", Complexity of Computer Computations, Proc. Sympos. IBM Thomas J. Watson Res. Center, Yorktown Heights, N.Y., New York: Plenum, pp. 85–103.
4. ^
5. ^ Chen, Jianer; Liu, Yang; Lu, Songjian; O'Sullivan, Barry; Razgon, Igor (2008), "A fixed-parameter algorithm for the directed feedback vertex set problem", Journal of the ACM, 55 (5): 1–19, doi:10.1145/1411509.1411511.
6. ^ Hecht, Michael (2017), "Exact localisations of feedback sets", Theory of Computing Systems, 62 (5): 1048–1084, arXiv:1702.07612, doi:10.1007/s00224-017-9777-6.
7. ^ Ausiello, G.; D'Atri, A.; Protasi, M. (1980), "Structure preserving reductions among convex optimization problems", Journal of Computer and System Sciences, 21 (1): 136–153, doi:10.1016/0022-0000(80)90046-X, MR 0589808.
8. ^ Kann, Viggo (1992), On the Approximability of NP-complete Optimization Problems (PDF), Ph.D. thesis, Department of Numerical Analysis and Computing Science, Royal Institute of Technology, Stockholm.
9. ^ a b Crescenzi, Pierluigi; Kann, Viggo; Halldórsson, Magnús; Karpinski, Marek; Woeginger, Gerhard (2000), "Minimum Feedback Arc Set", A compendium of NP optimization problems.
10. ^ Dinur, Irit; Safra, Samuel (2005), "On the hardness of approximating minimum vertex cover" (PDF), Annals of Mathematics, 162 (1): 439–485, doi:10.4007/annals.2005.162.439. (Preliminary version in Dinur, Irit (2002). "The importance of being biased". Proceedings of the thiry-fourth annual ACM symposium on Theory of computing - STOC '02. doi:10.1145/509907.509915..)
11. ^ Khot, Subhash; Regev, Oded (2008), "Vertex cover might be hard to approximate to within 2 − ε", Journal of Computer and System Sciences, 74 (3): 335–349, doi:10.1016/j.jcss.2007.06.019, MR 2384079.
12. ^ Even, G.; Naor, J.; Schieber, B.; Sudan, M. (1998), "Approximating minimum feedback sets and multicuts in directed graphs", Algorithmica, 20 (2): 151–174, doi:10.1007/PL00009191, MR 1484534.
13. ^ Berger, B.; Shor, P. (1990), "Approximation algorithms for the maximum acyclic subgraph problem", Proceedings of the 1st ACM-SIAM Symposium on Discrete Algorithms (SODA'90), pp. 236–243.
14. ^ Eades, P.; Lin, X.; Smyth, W. F. (1993), "A fast and eﬀective heuristic for the feedback arc set problem", Information Processing Letters, 47 (6): 319–323, doi:10.1016/0020-0190(93)90079-O.
15. ^ Kenyon-Mathieu, Claire; Schudy, Warren (2007), "How to rank with few errors: a PTAS for weighted feedback arc set on tournaments", Proc. 39th ACM Symposium on Theory of Computing (STOC '07), pp. 95–103, doi:10.1145/1250790.1250806, MR 2402432. See also author's extended version.
16. ^ Karpinski, M.; Schudy, W. (2010), "Faster algorithms for feedback arc set tournament, Kemeny rank aggregation and betweenness tournament", Proc. 21st ISAAC (2010), Lecture Notes in Computer Science, 6506, pp. 3–14, arXiv:1006.4396, doi:10.1007/978-3-642-17517-6_3.
17. ^ Hassin, Refael; Rubinstein, Shlomi (1994). "Approximations for the maximum acyclic subgraph problem". Information Processing Letters. 51 (3): 133–140. CiteSeerX 10.1.1.39.7717. doi:10.1016/0020-0190(94)00086-7.
18. ^ Kudelić, Robert (2016-04-01). "Monte-Carlo randomized algorithm for minimal feedback arc set problem". Applied Soft Computing. 41: 235–246. doi:10.1016/j.asoc.2015.12.018.
19. ^ Skiena, Steven (2010). The Algorithm Design Manual (2nd ed.). Springer Science+Business Media. pp. 348, 559–561. ISBN 978-1-849-96720-4.

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