# An Efficient Local Search for the Minimum Independent Dominating Set Problem

In the present paper, we propose an efficient local search for the minimum independent dominating set problem. We consider a local search that uses k-swap as the neighborhood operation. Given a feasible solution S, it is the operation of obtaining another feasible solution by dropping exactly k vertices from S and then by adding any number of vertices to it. We show that, when k=2, (resp., k=3 and a given solution is minimal with respect to 2-swap), we can find an improved solution in the neighborhood or conclude that no such solution exists in O(nΔ) (resp., O(nΔ^3)) time, where n denotes the number of vertices and Δ denotes the maximum degree. We develop a metaheuristic algorithm that repeats the proposed local search and the plateau search iteratively. The algorithm is so effective that it updates the best-known upper bound for nine DIMACS graphs.

## Authors

• 7 publications
04/17/2018

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## 1 Introduction

Let be a graph such that is the vertex set and is the edge set. Let and . A vertex subset is independent if no two vertices in are adjacent, and dominating if every vertex in is adjacent to at least one vertex in . Given a graph, the minimum independent dominating set (MinIDS) problem asks for a smallest vertex subset that is dominating as well as independent. The MinIDS problem has many practical applications in data communication and networks [13].

There is much literature on the MinIDS problem in the field of discrete mathematics [8]. The problem is NP-hard [6] and also hard even to approximate; there is no constant such that the problem can be approximated within a factor of in polynomial time, unless PNP [11].

For algorithmic perspective, Liu and Song [15] and Bourgeois et al. [4] proposed exact algorithms with polynomial space. The running times of Liu and Song’s algorithms are bounded by and , and the running time of Bourgeois et al.’s algorithm is bounded by , where is introduced to ignore polynomial factors. Laforest and Phan [14] proposed an exact algorithm based on clique partition, and made empirical comparison with one of the Liu and Song’s algorithms, in terms of the computation time. Davidson et al. [5] proposed an integer linear optimization model for the weighted version of the MinIDS problem (i.e., weights are given to edges as well as vertices, and the weight of an edge is counted as cost if the edge is used to assign a non-solution vertex to a solution vertex ; every non-solution vertex is automatically assigned to an adjacent solution vertex such that the weight of is the minimum) and performed experimental validation for random graphs. Recently, Wang et al. [20] proposed a tabu-search based memetic algorithm and Wang et al. [21] proposed a metaheuristic algorithm based on GRASP (greedy randomized adaptive search procedure). They showed their effectiveness on DIMACS instances, in comparison with CPLEX12.6 and LocalSolver5.5.

A vertex subset is an IDS iff it is a maximal independent set with respect to set-inclusion [2]. Then one can readily see that the MinIDS problem is equivalent to the maximum minimal vertex cover (MMVC) problem and the minimum maximal clique problem. Zehavi [22] studied the MMVC problem, which has applications to wireless ad hoc networks, from the viewpoint of fixed-parameter-tractability.

For a combinatorially hard problem like the MinIDS problem, it is practically meaningful to develop a heuristic algorithm to obtain a nearly-optimal solution in reasonable time. In the present paper, we propose an efficient local search for the MinIDS problem. By the term “efficient”, we mean that the proposed local search has a better time bound than one naïvely implemented. The local search can serve as a key tool of local improvement in a metaheuristic algorithm, or can be used in an initial solution generator for an exact algorithm. We may also expect that it is extended to the weighted version of the MinIDS problem in the future work.

Our strategy is to search for a smallest maximal independent set. Hereafter, we may call a maximal independent set simply a solution. In the proposed local search, we use -swap for the neighborhood operation. Given a solution , -swap refers to the operation of obtaining another solution by dropping exactly vertices from and then by adding any number of vertices to it. The -neighborhood of is the set of all solutions that can be obtained by performing -swap on . We call -minimal if its -neighborhood contains no such that .

To speed up the local search, one should search the neighborhood for an improved solution as efficiently as possible. For this, we propose -neighborhood search algorithms for and 3. When (resp., and a given solution is 2-minimal), the algorithm finds an improved solution or decides that no such solution exists in (resp., ) time, where denotes the maximum degree in the input graph.

Furthermore, we develop a metaheuristic algorithm named ILPS (Iterated Local & Plateau Search) that repeats the proposed local search and the plateau search iteratively. ILPS is so effective that, among 80 DIMACS graphs, it updates the best-known solution size for five graphs and performs as well as existing methods for the remaining graphs.

The paper is organized as follows. Making preparations in Section 2, we present -neighborhood search algorithms for and 3 in Section 3 and describe ILPS in Section 4. We show computational results in Section 5 and then give concluding remark in Section 6. Some proofs and experimental results are included in the appendix. The source code of ILPS is written in C++ and available at http://puzzle.haraguchi-s.otaru-uc.ac.jp/minids/.

## 2 Preliminaries

### 2.1 Notation and Terminologies

For a vertex , we denote by the degree of , and by the set of neighbors of , i.e., . For , we define . We denote by the subgraph induced by . The is called a -subset if .

Suppose that is an independent set. The tightness of is the number of neighbors of that belong to , i.e., . We call the -tight if its tightness is . In particular, a 0-tight vertex is called free. We denote by the set of -tight vertices. Then is partitioned into , where may be empty. Let denote the set of vertices that have the tightness no less than , that is, .

An independent set is a solution (i.e., a maximal independent set) iff . We call a solution vertex and a non-solution vertex. When a solution vertex and a -tight vertex are adjacent, is a solution neighbor of , or equivalently, is a -tight neighbor of .

A -swap on a solution is the operation of obtaining another solution such that is a -subset of and that is a non-empty subset of . We call a dropped subset and an added subset. The -neighborhood of is the set of all solutions obtained by performing a -swap on . A solution is -minimal if the -neighborhood contains no improved solution such that . Note that every solution is 1-minimal.

If a -subset is dropped from , then trivially, the solution vertices in become free, and some non-solution vertices may also become free. Observe that a non-solution vertex becomes free if the solution neighbors are completely contained in . We denote by the set of such vertices and it is defined as . Clearly the added subset should be a maximal independent set in . We have , and the tightness of any vertex in is at most (at the time before dropping from ).

### 2.2 Data Structure

We store the input graph by means of the typical adjacency list. We maintain a solution based on the data structure that Andrade et al. [1] invented for the maximum independent set problem. For the current solution , we have an ordering on all vertices in such that;

• whenever and ;

• whenever and ;

• whenever and ;

• whenever and .

Note that the ordering is partitioned into five sections; , , , and . In each section, the vertices are arranged arbitrarily. We also maintain the number of vertices in each section and the tightness for every non-solution vertex .

Let us describe the time complexities of some elementary operations. We can scan each vertex section in linear time. We can pick up a free vertex (if exists) in time. We can drop (resp., add) a vertex from (resp., to) the solution in time. See [1] for details.

Before closing this preparatory section, we mention the time complexities of two essential operations.

###### Proposition 1

Let be a -subset of . We can list all vertices in in time.

Proof: We let every have an integral counter, which we denote by . It suffices to scan vertices in twice. In the first scan, we initialize the counter value as for every neighbor of every solution vertex . In the second, we increase the counter of by one (i.e., ) when is searched in the adjacency list of . Then, if holds, we output as a member of since the equality represents that every solution neighbor of is contained in . Obviously the time bound is .

###### Proposition 2

Let be a -subset of . For any non-solution vertex , we can decide whether is adjacent to all vertices in in time.

Proof: We use the algorithm of Proposition 1. As preprocessing of the algorithm, we set the counter of each to 0, i.e., , which can be done in time. After we acquire by running the algorithm of Proposition 1, we can see if is adjacent to all other vertices in in time by counting the number of vertices such that and . If the number equals to (resp., does not equal to) , then we can conclude that it is true (resp., false).

## 3 Local Search

Assume that, for some , a given solution is -minimal for every . Such always exists, e.g., . In this section, we consider how we find an improved solution in the -neighborhood of or conclude that is -minimal efficiently.

Let us describe how time-consuming naïve implementation is. In naïve implementation, we search all -subsets of as candidates of the dropped subset , where the number of them is . Furthermore, for each , there are candidates of the added subset . The number of possible pairs is up to .

In the proposed neighborhood search algorithm, we do not search dropped subsets but added subsets; we generate a dropped subset from each added subset. When , the added subsets can be searched more efficiently than the dropped subsets. This search strategy stems from Proposition 3, a necessary condition of a -subset that the improvement is possible by a -swap that drops . We introduce the condition in Section 3.1.

Then in Section 3.2 (resp., 3.3), we present a -neighborhood search algorithm that finds an improved solution or decides that no such solution exists for (resp., 3), which runs in (resp., ) time.

### 3.1 A Necessary Condition for Improvement

Let be a -subset of . If there is a subset such that is maximal independent in and , then we have an improved solution . The connectivity of is necessary for the existence of such , as stated in the following proposition.

###### Proposition 3

Suppose that a solution is -minimal for every for some integer . Let be a -subset of . There is a maximal independent set in such that and only when the subgraph is connected.

Proof: Suppose that is not connected. Let be the number of connected components and be the subset of vertices in the -th component (, , , ). Each is not empty since otherwise there would be an isolated vertex in . It is a free vertex with respect to , which contradicts that is a solution. Then we have .

The maximal independent set is a subset of . We partition into , where . Each is maximal independent for the -th component. As , holds for some . Then we can construct an improved solution , which contradicts the -minimality of .

### 3.2 2-Neighborhood Search

Applying Proposition 3 to the case of , we have the following proposition.

###### Proposition 4

Let be a 2-subset of . There is a non-solution vertex in such that is a solution only when there is a 2-tight vertex in .

We can say more on Proposition 4. The vertex should be 2-tight since, if not so (i.e., is 1-tight), would not be maximal independent for ; is adjacent to only one of from the definition of 1-tightness.

In summary, if there is an improved solution , then is 2-tight and has and as the solution neighbors. Instead of searching all 2-subsets of , we scan all 2-tight vertices, and for each 2-tight vertex , we take as the candidate of the dropped set. We have the following theorem.

###### Theorem 1

Given a solution , we can find an improved solution in the 2-neighborhood or conclude that is 2-maximal in time.

Proof: Since we maintain the solution by means of the vertex ordering, we can scan all the 2-tight vertices in time. For each 2-tight , we can detect the two solution neighbors, say and , in time.

Let . The singleton is maximal independent for and thus we have an improved solution iff is adjacent to all other vertices in . Whether is adjacent to all other vertices in is decided in time, as we stated in Proposition 2. If it is the case, then we can construct an improved solution in time as the vertex ordering takes time to drop from and time to add to it [1]. Otherwise, we can conclude that is not a solution because some vertices in are not dominated.

We have seen that, for each 2-tight vertex , it takes time to find an improved solution or to conclude that it is not a solution. Therefore, the overall running time is bounded by .

### 3.3 3-Neighborhood Search

We have the following proposition by applying Proposition 3 to the case of .

###### Proposition 5

Suppose that is a 2-minimal solution and that is a -subset of . There is a subset of such that is maximal independent in and only when either of the followings holds:

(a)

there is a 3-tight vertex in that has , and as the solution neighbors;

(b)

there are two 2-tight vertices in such that one has and as the solution neighbors and the other has and as the solution neighbors.

Let us make observation on the added subset. Suppose that, for an arbitrary 3-subset , there is such that is maximal independent in and . When , the only vertex in is 3-tight since otherwise some vertex in would not be dominated. When , at least one of the two vertices in is either 2-tight or 3-tight; if both are 1-tight, one vertex of would not be dominated. Concerning the tightness, the following four situations are possible:

(i)

and is 3-tight;

(ii)

, is 3-tight, and is -tight such that ;

(iii)

, is 2-tight, and is 2-tight;

(iv)

, is 2-tight, and is 1-tight.

From (ii) to (iv), the vertices and are not adjacent. We illustrate (i) to (iv) in Figure 1.

Based on the above, we summarize the search strategy as follows. In order to generate all 3-subsets of such that satisfies either (a) or (b) of Proposition 5, we scan all 3-tight vertices (Proposition 5 (a)) and all pairs of 2-tight vertices, say and , such that (Proposition 5 (b)). For (a), we take and search for a 1- or 2-subset that is maximal independent in , regarding the 3-tight vertex as the vertex in (i) and (ii). Similarly, for (b), we take and search for a 2-subset that is maximal independent in , regarding the 2-tight vertex as the vertex in (iii) and (iv).

We have the following theorem on the time complexity of 3-neighborhood search. The proof is included in the appendix.

###### Theorem 2

Given a 2-minimal solution , we can find an improved solution in the 3-neighborhood or conclude that is 3-maximal in time.

## 4 Iterated Local & Plateau Search

In this section, we present a metaheuristic algorithm named ILPS (Iterated Local & Plateau Search) that repeats the proposed local search and the plateau search iteratively.

We show the pseudo code of ILPS in Algorithm 1. The ILPS has four parameters, that is , , and , where is an initial solution, is the order of the local search (i.e., a -minimal solution is searched by in Line 6), and and are integers. The roles of the last two parameters are mentioned in Section 4.2.

The in Line 6 is the subroutine that returns a -minimal solution from an initial solution , where is set to either two or three. When , it determines a 2-minimal solution by moving to an improved solution repeatedly as long as the 2-neighborhood search algorithm delivers one. When , it first finds a 2-minimal solution, and then runs the 3-neighborhood search algorithm. If an improved solution is delivered, then the local search moves to the improved solution and seeks a 2-minimal one again since the solution is not necessarily 2-minimal. Otherwise, the current solution is 3-minimal.

Below we explain two key ingredients: the plateau search and the vertex penalty. We describe these in Sections 4.1 and 4.2 respectively. We remark that they are inspired by Dynamic Local Search for the maximum clique problem [19] and Phased Local Search for the unweighted/weighted maximum independent set and minimum vertex cover [18].

### 4.1 Plateau Search

In the plateau search (referred to as in Line 7), we search solutions of the size that can be obtained by swapping a solution vertex and a non-solution vertex . Let be the collection of all solutions that are obtainable in this way. The size of any solution in is . We execute for every solution , and if we find an improved solution such that , then we do the same for , i.e., we execute for every solution . We repeat this until no improved solution is found and employ a best solution among those searched as the output of the plateau search.

We emphasize the efficiency of the plateau search; all solutions in can be listed in time. Observe that is a solution iff is 1-tight such that is the only solution neighbor of , and is adjacent to all vertices in other than . We can scan all 1-tight vertices in time. For each 1-tight vertex , the solution neighbor is detected in time, and whether the last condition is satisfied or not is identified in time from Proposition 2. Dropping from and adding to can be done in time.

### 4.2 Vertex Penalty

In order to avoid the search stagnation, one possible approach is to apply a variety of initial solutions. To realize this, we introduce a penalty function on the vertices. The penalty function is initialized so that for all (Line 3). During the algorithm, is managed by the subroutine UpdatePenalty (Lines 4 and 12). When the initial solution of the next local search is determined, it increases the penalty of every vertex by one, i.e., . Furthermore, to “forget” the search history long ago, it reduces to for all in every iterations. This is the third parameter of ILPS and called the penalty delay.

The is used in the subroutine Kick (Line 11), the initial solution generator, so that vertices with fewer penalties are more likely to be included in the initial solution. Kick generates an initial solution by adding non-solution vertices (with respect to the incumbent solution ) “forcibly” to . The added vertices are chosen one by one as follows; in one trial, Kick

picks up one non-solution vertex. It then goes on to the next trial with the probability

or stops the selection with the probability , where is the fourth parameter of ILPS. Observe that specifies the expected number of added vertices. In the first trial, Kick randomly picks up a non-solution vertex that has the fewest penalty. In a subsequent -th trial , let be the set of vertices chosen so far. Kick samples three vertices randomly from , and picks up the one that has the fewest penalty among the three. Suppose that has been picked up as the result of trials. Then we construct an independent set . The may not be a solution as there may remain free vertices. If so, we repeatedly pick up free vertices by the maximum-degree greedy method until becomes a solution. We use the acquired as the initial solution of the next local search.

## 5 Computational Results

We report some experimental results in this section. In Section 5.1

, to gain insights into what kind of instance is difficult, we examine the phase transition of difficulty with respect to the edge density. The next two subsections are devoted to observation on the behavior of the proposed method. In Section

5.2, we show how a single run of improves a given initial solution. In Section 5.3, we show how the penalty delay affects the search. Finally in Section 5.4, we compare ILPS with the memetic algorithm [20], GRASP+PC [21], CPLEX12.6 [12] and LocalSolver5.5 [16] in terms of the solution size, using DIMACS graphs.

All the experiments are conducted on a workstation that carries an Intel Core i7-4770 Processor (up to 3.90GHz by means of Turbo Boost Technology) and 8GB main memory. The installed OS is Ubuntu 16.04. Under this environment, it takes 0.25 s, 1.54 s and 5.90 s approximately to execute dmclique (http://dimacs.rutgers.edu/pub/dsj/clique/) for instances r300.5.b, r400.5.b and r500.5.b, respectively. The ILPS algorithm is implemented in C++ and compiled by the g++ compiler (ver. 5.4.0) with -O2 option.

### 5.1 Phase Transition of Difficulty

The phase transition has been observed for many combinatorial problems [7, 9, 10]. Roughly, it is said that over-constrained and under-constrained instances are relatively easy, and that intermediately constrained ones tend to be more difficult.

In the MinIDS problem, the amount of constraints is proportional to the edge density . We examine the change of difficulty with respect to

. We estimate the difficulty of an instance by how long CPLEX12.8 takes to solve it.

For each , we generate 100 random graphs (Erdös-Rényi model) with vertices and the edge density , i.e., an edge is drawn between two vertices with probability . We solve the 100 instances by CPLEX12.8 and take the averaged computation time. We set the time limit of each run to 60 s. If CPLEX12.8 terminates by the time limit, then we regard the computation time as 60 s.

Figure 2 shows the result. We may say that instances with the edge densities from 0.1 to 0.4 are likely to be more difficult than others. In fact, the experiments in [5, 14] mainly deal with random graphs with the edge densities in this range.

### 5.2 A Single Run of Local Search

We show how a single run of improves an initial solution . Again we take a random graph. We fix the number of vertices to . For every , we generate random graphs. Then for each graph, we run five times, where we use different random seeds in each time and construct the initial solution randomly.

We show the averaged sizes of random, 2-minimal and 3-minimal solutions in Table 1. We see that, the larger the edge density is, the fewer the solution size becomes. The local search improves a random solution to some extent. improves the solution more than . The difference between the two local searches is the largest when , that is . The difference gets smaller when gets larger. In particular, when , we see no difference.

Let us discuss computation time. In the left of Figure 3, we show how the averaged computation time changes with respect to . We see that the computation time of is tens to thousands of times the computation time of . However, it does not necessarily diminish the value of the 3-neighborhood search. As will be shown in Section 5.4, when , ILPS can find such a good solution that is not obtained by .

In general, for a fixed , it takes more computation time when is larger. Recall Theorem 1 (resp., 2); when (resp., 3), the -neighborhood search algorithm finds an improved solution for the current solution or concludes that is -minimal in (resp., ) time. Roughly, is increasing as gets larger.

For , we attribute the peak at to the number of 3-tight vertices. In the right of Figure 3, We show the averaged numbers of 2- and 3-tight vertices with respect to 3-minimal solutions. The 3-neighborhood search algorithm searches 2- and 3-tight vertices. The numbers of both vertices are generally non-decreasing from to 0.8, but when , the number of 3-tight vertices decreases dramatically. This is due to the solution size. The solution size gives an upper bound on the tightness of any non-solution vertex, and when , the averaged size of a 3-minimal solution is less than three; see Table 1. Since most of the non-solution vertices are either 1- or 2-tight, we hardly handle the situations (i) and (ii) in Section 3.3.

### 5.3 Penalty Delay

We introduced the notion of vertex penalty to control the search diversification. When the penalty delay is larger, more varieties of initial solutions are expected to be tested in ILPS.

To illustrate the expectation, we evaluate how many iterations ILPS takes until all vertices are covered by the initial solutions, that is, used in the initial solutions at least once. The solid line in Figure 4 shows the number of iterations taken to cover all vertices. The graph we employ here is a grid graph such that each vertex is associated with a 2D integral point , and that two vertices and are adjacent iff . For each , the number of iterations is averaged over 500 runs of ILPS with different random seeds, where we fix and construct the first initial solution by the maximum-degree greedy algorithm.

The observed phenomenon meets our expectation; The number is non-increasing with respect to and saturated for . In other words, when is larger, more varieties of initial solutions are generated in a given number of iterations.

However, setting to a large value does not necessarily lead to discovery of better solutions. The dashed line in Figure 4 shows the averaged number of iterations that ILPS takes to find an optimal solution; we know that the optimal size is 24 since we solve the instance optimally by CPLEX. When , the number is approximately decreasing and takes the minimum at , but a larger does not make any improvement. Hence, given an instance, we need to choose an appropriate value of carefully.

### 5.4 Performance Validation

We run ILPS algorithm for 80 DIMACS instances that are downloadable from [17]. We generate the first initial solution by the maximum-degree greedy method, and fix the parameter to three. For , all pairs in are tested. For each instance and each , we run ILPS algorithm 10 times, using different random seeds. We terminate the algorithm by the time limit. The time limit is set to 200 s. When , we modify Algorithm 1 so that in Line 7 is called only when as the plateau search is rather time-consuming.

We take four competitors from [20] and [21]. The first is MEM, a tabu-search based memetic algorithm in [20]. The second is GP, the GRASP+PC algorithm in [21]. The third is CP, which stands for CPLEX12.6 [12] that solves an integer optimization model of the MinIDS problem. The fourth is LS, which stands for LocalSolver5.5 [16], a general discrete optimization solver based on local search. MEM is run on a computer with a 2.0GHz CPU and a 4GB memory, whereas the other competitors are run on computers with a 2.3GHz CPU and an 8GB memory. The time limit of MEM and GP is set to 200 s, and that of CP and LS is set to 3600 s.

In Table 2, we show the results on selected instances. The columns “” and “” indicate the number of vertices and the edge density, respectively. The edge density is between 0.1 and 0.5 in all instances except hamming8-2. In our context, the instances are expected to be difficult. For ILPS, we show the results for in detail, regarding this pair as the representative. The columns “Min” and “Max” indicate the minimum/maximum solution size over 10 runs, and the column “Avg” indicates the average. The column “TTB” indicates the time to best (in seconds), that is, the average of the computation time that ILPS takes to find the solution of the size “Min”. The symbol represents that the time is less than 0.1 s. The column “Best” indicates the minimum solution size attained over all . The rightmost four columns indicate the solution size attained by the competitors. The symbol before the instance name indicates that the solution size attained by CPLEX is optimal.

The table contains only results on the 13 selected instances such that the solutions sizes attained by “Best”, “MEM” and “GP” are not-all-equal, except hamming8-2. We guarantee that, for the remaining 67 instances, ILPS’s “Best” is as good as any competitor. The boldface indicates that the solution size is strictly smaller than those of the competitors. Then we update the best-known solution size in five graphs. These show the effectiveness of the proposed local search and the ILPS algorithm. All results are included in the appendix.

For hamming8-2, when , ILPS cannot find a solution of the optimal size 32 for any penalty delay . However, when , ILPS finds an optimal solution with , and .

Before closing this section, let us report our preliminary results briefly.

• A preliminary version of ILPS happened to find a solution of the size 31 for C2000.9 and a solution of the size 15 for keller6. See the detail for the appendix.

• Let us consider a finer swap operation, -swap, that obtains another solution by dropping exactly vertices from the current one and then by adding exactly vertices to it. One can prove that, given a solution and a constant , we can improve by -swap or conclude that it is not possible in time. We implemented -swap in a preliminary version of ILPS, but it does not yield significant improvement even when is set to a constant larger than three.

• We tested Laforest and Phan’s exact algorithm [14], and found that the algorithm is not suitable for a task of finding a good solution quickly. The source code is available at http://todo.lamsade.dauphine.fr/spip.php?article42.

• BHOSLIB [3] is another well-known collection of benchmark instances. It contains 36 instances such that is between 450 and 4000 and that is no less than 0.82. Hence, the BHOSLIB instances are expected to be easy in our context. The ILPS with finds a solution of the size three for all the instances. We also run CPLEX12.8 for 200 s, generating an initial solution by the maximum-degree greedy algorithm. CPLEX12.8 finds a solution of the size five for frb100-40, and a solution of the size three for the other instances. In addition, the solution of the size three is proved to be optimal for 15 instances whose names start with frb30, frb35 and frb40.

## 6 Concluding Remark

We have considered an efficient local search for the MinIDS problem. We proposed fast -neighborhood search algorithms for and 3, and developed a metaheuristic algorithm named ILPS that repeats the local search and the plateau search iteratively. ILPS is so effective that it updates the best-known solution size in five DIMACS graphs.

The proposed local search is applicable to other metaheuristics such as genetic algorithms, as a key tool of local improvement. The future work includes an extension of the local search to a weighted version of the MinIDS problem.

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## Appendix

### Proof of Theorem 2

For preparation, let us introduce the following proposition. This is a generalization of Proposition 2 in the sense that the vertex subset is taken arbitrarily. The proof is similar to Proposition 2.

###### Proposition 6

Given an arbitrary vertex subset and a vertex , we can decide whether is adjacent to all vertices in in time.

Proof: We let every have another integral variable, which we denote by . Initially, is set to zero. We also maintain a global integral variable that is set to one initially.

First, for all , we set to the current (i.e., ). We then count the number of vertices in such that . If the number equals to , then we can regard that is adjacent to all vertices in . As postprocessing, we increase by one (i.e., ).

An integral variable is bounded in conventional programming languages. If reaches the upper limit (e.g., INT_MAX in C), then we reset to zero for all and to one again.

We prove Theorem 2 in Section 3.3. If there is an improved solution , then the four situations from (i) to (iv) are possible as to the tightnesses of vertices in . (For illustration, see Figure 1.) Given a non-solution vertex in (i) to (iv), the following Lemmas 1 to 4 show time complexities of finding an improved solution or concluding that no such solution exists, respectively.

###### Lemma 1

Suppose that a 3-tight vertex is given. Let be the set of solution neighbors of . We can decide whether is a solution or not in time.

Proof: The set can be decided in time. It suffices to decide whether is adjacent to all vertices in . This can be done in time from Proposition 2.

###### Lemma 2

Suppose that a 3-tight vertex is given. Let be the set of solution neighbors of . We can find a non-solution vertex such that is a solution or conclude that such does not exist in time.

Proof: Let be the subset of such that the vertices in are not adjacent to . The sets , and can be constructed in time. All we have to do is to check whether there is such that is adjacent to all vertices in . From Proposition 6 and , this can be done in time.

###### Lemma 3

Suppose that a 2-tight vertex is given. We can decide in time whether there exists a 2-tight vertex such that:

• and have exactly one solution neighbor in common;

• is a solution, where .

Proof: Let be the set of solution neighbors of . The target 2-tight vertex should be a neighbor of either or , but not both, as and have exactly one solution neighbor in common. Hence, there are at most candidates for .

For each candidate of , let be the set of solution neighbors of . (If , then we discard this candidate.) Let . To check whether is a solution, it suffices to check whether is a solution in the subgraph . It takes time to decide , to decide whether is independent, and decide whether is dominating the vertices in .

###### Lemma 4

Suppose that a 2-tight vertex is given. We can decide in time whether there exist a 2-tight vertex and a 1-tight vertex such that:

• and are adjacent, and have exactly one solution neighbor in common. Let and be the sets of solution neighbors of and respectively;

• the unique solution neighbor of is ;

• is a solution, where .

Proof: Similarly to Lemma 3, there are at most candidates for . The adjacency between and can be checked in time.

Note that the number of candidates for is also at most . Each candidate of has at most 1-tight neighbors that are the candidates of . Hence, for , there are candidates.

For each candidate of , to check whether is a solution, it suffices to check whether is a solution in the subgraph , which can be done in time. Then we have the time bound .

(Proof of Theorem 2) For every 3-tight vertex , check whether there is an improved solution in the situations (i) and (ii). This can be done in time from Lemmas 1 and 2. Similarly, for every 2-tight vertex , check whether there is an improved solution in the situations (iii) and (iv). This can be done in time from Lemmas 3 and 4.

As there are non-solution vertices, we have the time bound .

### All Computational Results on DIMACS Graphs

The next Table 3 shows all results on 80 DIMACS graphs that are downloadable from [17]. The column “CP12.8” represents CPLEX12.8. We run CPLEX12.8 for each instance, setting the time limit to 200 seconds. An initial solution is constructed by the maximum-degree greedy algorithm. A hyphen in the rightmost four columns indicates that the corresponding result is not available in [20, 21].

As mentioned in the paper, we happened to find a solution of the size 31 for C2000.9 and a solution of the size 15 for keller6 by a preliminary version of ILPS. The vertices in the solution for C2000.9 have the following IDs:

23, 78, 161, 252, 279, 344, 441, 556, 662, 671, 703, 769, 847, 864, 926, 952, 1056, 1266, 1274, 1475, 1540, 1619, 1636, 1641, 1646, 1673, 1826, 1839, 1915, 1947, 1979.

The solution for keller6 is the set of vertices with the following IDs:

169, 601, 659, 855, 1020, 1215, 1352, 1586, 2052, 2376, 2463, 2818, 2847, 2944, 3281.

By the ID of a vertex, we mean an integer that is given to the vertex in the DIMACS files.