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Minimum cuts with restrictions

There is a nice algorithm design technique for cuts: fix a small number of partial solutions, and guess all possible solutions built from the partial solution. Here are two demonstrations.

1 k-size-cut

A cut (X,\bar{X}) such that both |X|,|\bar{X}|\geq k is called a k-size-cut.


Given a graph G, find a minimum k-size-cut.

The standard algorithm is based on finding all possible min-ST-cut, where S,T are all possible disjoint sets of size k. The running time is O(n^{2k}) min-cut computations.

One can improve it by first fix an arbitrary set of k vertices Y. Consider a min-k-size-cut (X,\bar{X}). Let S'=X\cap Y and T'=\bar{X}\cap Y. We can try to find the min-cut of all possible S and T such that S'\subset S,T'\subset T and |S|=|T|=k. Since we don't know what S' and T' are, we will try all 2^k possibilities. Here the S' and T' are the partial solutions. This gives us an algorithm with running time O(2^k n^k) min-cut computations. The idea is also used in computing matroid connectivity.

A further improvement depending on the fact that there are only O(n^{k-1}) cuts we try to avoid. We can enumerate all the cuts from smallest to largest, with a delay of a single application of Hao-Orlin algorithm [1]. The running time of Hao-Orlin algorithm is approximately a single maximum flow. One of the smallest {n \choose k-1}+1 cuts is the min-k-size-cut. The running time is O(n^{k-1}) Hao-Orlin computation [2].

It's interesting to wonder if the running time can be improved, especially for the case where k=2. Fix a set S=\{s,t\} of size 2. There are two cases, either the min-2-size-cut crosses S, then one of the the 3 smallest st-cuts is our solution. The other case is S is on one side of the min-2-size-cut, and we are interested in finding a cut so the side doesn't contain S has at least 2 vertices.

This prompt a interesting problem:


Find the second smallest st-cut, if we already have a min-st-cut and it's corresponding flow(or some other useful information obtained through a push relabel flow computation).

Can we solve this in O(m) time? What if we also know the min-st-cut is induced by t?

2 2-restricted-cut

A problem which appeared as a question on cstheory.


Given a graph G, find the minimum cut under the constraint that each side is connected and has at least 2 vertices. (Assume it exists).

This is the k-restricted edge connectivity problem when k=2.

\lambda_k(G), the k-restricted edge connectivity of G, defined as the smallest number of edges such that the removal result exactly 2 connected component, each with at least k vertices. The rest of the article describes the algorithm by Esfahanian and Hakimi [3].

\lambda_2(G) can be found in O(m^2) flow computations. The idea is to contract any two independent edges e and e' to s and t, and then find a st-min-cut. The cut will give us the desired partition.

It can be improved with the idea of fixing a partial solution. Consider a single edge e that incident to a vertex with lowest degree, contract it to vertex s. Pick another edge e' that not incident to s, we contract it to t. The min-cut between s and t reflects a 2-restricted cut. If e is on one side of the min-2-restricted cut, then this algorithm finds it in O(m) flow computations by trying all possible e'.

Otherwise, e is an edge crossing every min-2-restricted cut. Let e=uv and and wlog \deg(u)=\delta, the min degree. We fix another partial solutions where u and v are on different side of the min-2-restricted cut. One can contract any edge incident to u and any edge incident to v and apply a flow computation. There are at most \deg(u) \deg(v)\leq \delta n = O(m) flow computations.


[1] J. Hao, J. Orlin, A faster algorithm for finding the minimum cut in a directed graph, Journal of Algorithms. 17 (1994) 424–446 10.1006/jagm.1994.1043.

[2] L.-P. Yeh, B.-F. Wang, H.-H. Su, Efficient algorithms for the problems of enumerating cuts by non-decreasing weights, Algorithmica. 56 (2009) 297–312 10.1007/s00453-009-9284-5.

[3] A.-H. Esfahanian, S. Hakimi, On computing a conditional edge-connectivity of a graph, Information Processing Letters. 27 (1988) 195–199 10.1016/0020-0190(88)90025-7.

Posted by Chao Xu on 2016-04-24.
Tags: cut.