Improved Analysis of HighestDegree Branching
for Feedback Vertex Set
Abstract
Recent empirical evaluations of exact algorithms for Feedback Vertex Set have demonstrated the efficiency of a highestdegree branching algorithm with a degreebased pruning heuristic. In this paper, we prove that this empirically fast algorithm runs in time, where is the solution size. This improves the previous best time deterministic algorithm obtained by Kociumaka and Pilipczuk.
1 Introduction
Feedback Vertex Set (FVS) is a classical NPhard graph optimization problem of finding the minimumsize vertex deletion set to make the input graph a forest. It is known that this problem is fixedparameter tractable (FPT) parameterized by the solution size ; i.e., we can find a deletion set of size in ^{1}^{1}1 hides factors polynomial in . Note that for FVS, any time FPT algorithms can be improved to time by applying a lineartime kernel [10] as a preprocess. We can therefore focus only on the factor when comparing the running time. time for some function . FVS is one of the most comprehensively studied problems in the field of parameterized algorithms, and various FPT algorithms using different approaches have been developed, including shortcycle branching [7], highestdegree branching [2], iterativecompression branching [4, 3, 14], LPguided branching [11, 12], cutandcount dynamic programming [5], and random sampling [1].
The current fastest deterministic FPT algorithm for FVS is a branching algorithm combined with the iterative compression technique [14] which runs in time. When allowing randomization, the current fastest one is a cutandcount dynamic programming algorithm [5] which runs in time. In this paper, we give a faster deterministic algorithm which runs in time. As explained below, this study is strongly motivated by Parameterized Algorithms and Computational Experiments (PACE) challenge and its followup empirical evaluation by Kiljan and Pilipczuk [13]. Instead of designing a new theoretically fast algorithm, we analyze the theoretical worstcase running time of the empirically fast algorithm that has been developed through the PACE challenge and the empirical evaluation, and we show that this algorithm is not only empirically fast but also theoretically fast.
PACE challenge is an annual programming challenge started in 2016. Due to its importance in the field, FVS was selected as the subject of track B in the first PACE challenge [6]. Although in theoretical studies, the current fastest algorithm is the randomized cutandcount dynamic programming, the result of the challenge suggests that branching is the best choice in practice. This is not so surprising; because the theoretical analysis of branching algorithms is difficult, the proved upper bound of the running time is rather pessimistic.
Among seven submissions to the PACE challenge, top six submissions used branching algorithms; the first used the LPguided branching; the second and third used branching on highestdegree vertices; the fourth and sixth used branching combined with iterative compression; and the fifth used branching on short cycles. In addition to the pruning by the LP lower bound, the firstplace solver by Imanishi and Iwata [9] used the following degreebased pruning heuristic:
Lemma 1 ([9]).
Given a set of undeletable vertices , let be the vertices of in the nondecreasing order of the degrees in . If holds, there is no feedback vertex set of size .
The followup empirical evaluation [13] shows that the use of the degreebased pruning is much more important than the choice of branching rules. By combining with the degreebased pruning, the performances of the LPguided branching [11], the highestdegree branching [2], and the iterativecompression branching [14], are all significantly improved, and among them, the highestdegree branching slightly outperforms the others. Cao [2] showed that one can stop the highestdegree branching at depth by using a degreebased argument, and therefore the running time is . On the other hand, the theoretically proved running time of other branching algorithms (without the degreebased pruning) are, for the LPguided branching [11] and for the iterativecompression branching [14]. These affairs motivated us to refine the analysis of the highestdegree branching with the degreebased pruning.
In our analysis, instead of bounding the depth of the search tree as Cao [2] did, we design a new measure to bound the size of the search tree. The measure is initially at most and we show that the measure drops by some amount for each branching. In contrast to the standard analysis of branching algorithms, our measure has a negative term and thus can have negative values; however, we show that we can immediately apply the degreebased pruning for all such cases. A simple analysis already leads to an time upper bound which significantly improves the time upper bound obtained by Cao [2]. We then apply the measureandconquer analysis [8] and improve the upper bound to .
1.1 Organization
Section 2 describes the highestdegree branching algorithm with the degreebased pruning. In Section 3, we analyze the running time of the algorithm. We first give a simple analysis in Section 3.1 and then give a measureandconquer analysis in Section 3.2. While the correctness of the simple analysis can be easily checked, we need to evaluate thousands of inequalities to check the correctness of the measureandconquer analysis. For convenience, we attach a source code of the program to evaluate the inequalities in Appendix A. The same source code is also available at https://github.com/wataorz/FVS_analysis.
2 Algorithm
An input to the algorithm is a tuple of a multigraph , a set of undeletable vertices , and an integer . Our task is to find a subset of vertices such that and contains no cycles. Note that a double edge is also considered as a cycle. We denote by the multiset of the adjacent vertices of and define . For convenience, we use to denote .
Our algorithm uses the standard reduction rules listed below in the given order (i.e., rule is applied only when none of the rules with are applicable). All of these reductions are also used in the empirical evaluation by Kiljan and Pilipczuk [13]. If none of the reductions are applicable, we apply a pruning rule, and if it cannot be pruned, we apply a branching rule.
Reduction Rule 1.
If there exists a vertex of degree at most one, delete .
Reduction Rule 2.
If there exists a vertex such that contains a cycle, delete and decrease by one.
Reduction Rule 3.
If there exists a vertex of degree two, delete and add an edge connecting its two endpoints.
Reduction Rule 4.
If there exists an edge of multiplicity more than two, reduce its multiplicity to two.
Reduction Rule 5.
If there exists a vertex incident to a double edge with , delete and decrease by one.
Reduction Rule 6.
If , solve the problem in polynomial time by a reduction to the matroid matching [3].
Pruning Rule.
If or , return NO.
Branching Rule.
Pick a vertex of the highest degree . Let and let be the graph obtained by contracting into a single vertex . We branch into two cases: and .
The correctness of the first four reduction rules is trivial. We can prove the correctness of the reduction rule 5 as follows. Because there is a double edge , any feedback vertex set must contain at least one of and . Because has at most one edge other than the double edge , every cycle containing also contains . Therefore, there always exists a minimum feedback vertex set containing .
After applying the reductions, the following conditions hold.

has the minimum degree at least three.

No double edges are incident to .

For any vertex , has the minimum degree at least two.

.
The correctness of the pruning rule follows from the following lemma.
Lemma 2.
If the minimum degree of is at least two and holds, there is no feedback vertex set of size at most .
Proof.
Suppose that there is a feedback vertex set of size at most . We have
Because is a forest, this must be nonnegative. ∎
Note that this pruning is different from the degreebased pruning (Lemma 1) used in the ImanishiIwata solver [9] and the empirical evaluation [13]; however, as the following lemma shows, if this pruning is applied, then the original degreebased pruning is also applied. Therefore, we can use the same analysis against the original degreebased pruning. We use this weaker version because it is sufficient for our analysis. We leave whether the stronger version helps further improve the analysis as future work.
Lemma 3.
For a subset , let be the vertices of in the nonincreasing order of the degrees in . If the minimum degree of is at least two, implies .
Proof.
Let and assume that . Then, we have
3 Analysis
For parameters and satisfying , we define
Initially, we have .
Lemma 4.
After the pruning, we have .
Proof.
We now show that applying the reduction rules does not increase . We can easily see that never increases by the reduction but may decrease; however, because such decrease leads to a smaller , we can analyze as if does not change by the reduction. Because the reduction rule 3 deletes a vertex of degree two and does not change the degrees of other vertices, it does not change . Because the reduction rule 4 is applied only when the reduction rule 2 cannot be applied, it does not change the degrees of vertices in , and therefore it does not change . Because the graph immediately after branching has the minimum degree at least two, we apply the reduction rule 1 only after applying the reduction rules 2 or 5.
Lemma 5.
The reduction rule 2 or 5, together with the subsequent applications of the reduction rule 1, does not increase .
Proof.
By deleting an edge such that and , increases by at most
In the series of the reductions, the number of such deletion is at most . Because decreases by one, the increase of is at most . ∎
Finally, we analyze the branching rule. Let and be the multiset of degrees of vertices in . The degree of is . In the former case of the branching, we have
In the latter case, we have
If holds for any for some , the running time of the algorithm is bounded by . We now optimize the parameters to minimize .
3.1 Simple Analysis
As a simple analysis whose correctness can be easily checked, we use , for all , and . Note that holds. For these parameters, we have
We now show that holds by the following case analysis.
3.2 MeasureandConquer Analysis
We use the parameters , shown in Table 1, and .
1  0.000000 

2  0.000000 
3  0.114038 
4  0.186479 
5  0.238143 
6  0.277239 
7  0.308030 
8  0.332974 
9  0.353536 
10  0.370540 
11  0.384771 

12  0.396884 
13  0.408715 
14  0.418855 
15  0.427643 
16  0.435333 
17  0.442118 
18  0.448149 
19  0.453544 
20  0.458401 
21  0.462794 

22  0.466788 
23  0.470435 
24  0.473778 
25  0.476853 
26  0.479691 
27  0.482320 
28  0.484760 
29  0.487032 
0.489153 
Lemma 6.
holds for any with .
Proof.
Suppose that holds for some . Because for all and because holds, decreasing by one does not change nor . Therefore, we can focus on the case of for all . We now show that the inequality holds by induction on . When , we can verify that
(1) 
holds by naively enumerating all the possible configurations of . Assume that, for a fixed , the inequality holds for any . We show that the inequality also holds for any .
When , we have
and
where . We can verify that our parameters satisfy
(2) 
Therefore, we have . This shows that
When , let . We have
and
Because we can verify that our parameters satisfy
(3) 
we have . This shows that
∎
Acknowledgements.
We would like to thank Yixin Cao for valuable discussions and thank organizers of PACE challenge 2016 for motivating us to study FVS.
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Appendix A Program to check Lemma 6
We attach a source code of a python3 program to evaluate the inequalities (1)–(3) appeared in the proof of Lemma 6. The same source code is also available at https://github.com/wataorz/FVS_analysis.