# Discrete Applied Mathematics. The firefighter problem with more than one firefighter on trees

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2 900 C. Bazgan et al. / Discrete Applied Mathematics 161 (2013) Moreover, the firefighter problem is NP-hard for cubic graphs [13]. From the approximation point of view, the firefighter problem is e -approximable on trees [3] and it is not e 1 n1 ε -approximable on general graphs for any ϵ (0, 1) [2], if P NP. Moreover for trees where vertices have at most three children, the firefighter problem is approximable [11]. Finally, the firefighter problem is polynomial-time solvable for caterpillars and P-trees [14]. A problem related to the firefighter problem, denoted by S-Fire, was introduced in [13]. It consists of deciding if there is a strategy of choosing a vertex to be protected at each time step such that all vertices of a given set S are saved. S-Fire was proved to be NP-complete for trees of maximum degree three in which every leaf is at the same distance from the vertex where the fire starts and S is the set of leaves. In this paper, we consider a generalized version of S-Fire. We denote by (S, b)-fire, where b 1 is an integer, the problem which consists of deciding if we can choose at most b vertices to be protected by firefighters at each time step and save all the vertices from a given set S. Thus, S-Fire is equivalent to (S, 1)-Fire. The optimization version of (S, b)-fire will be denoted by Max (S, b)-fire. This problem consists of choosing at most b vertices to be protected at each time step and saving as many vertices as possible from a given set S. Hence, Max (S, 1)-Fire corresponds to the firefighter problem when S is the set of all vertices of the graph. Max (S, b)-fire is known to be 2-approximable for trees when S is the set of all vertices [10]. A survey on the firefighter problem and related problems can be found in [8]. In this survey, the authors presented a list of open problems. Here, we will answer three of these open questions (questions 2, 4, and 8). The first question asks for finding algorithms and complexity results of (S, b)-fire when b 2. We show that for any fixed b 2, (S, b)-fire is NP-complete for trees of maximum degree b + 2 when S is the set of all leaves. Moreover, we show that for any fixed b 2, Max (S, b)-fire is NP-hard for trees of maximum degree b + 3 when S is the set of all vertices. Finally, we show that for any b 1, (S, b)-fire is polynomial-time solvable for trees of maximum degree b + 2 when the fire breaks out at a vertex of degree at most b + 1. The second question asks if there exists a constant c > 1 such that the greedy strategy of protecting, at each time step, a vertex of highest degree adjacent to a burning vertex gives a polynomial-time c-approximation for the firefighter problem on trees. We give a negative answer to this question. Finally, the third question asks for finding classes of trees for which the firefighter problem can be solved in polynomial time. We present a polynomial-time algorithm to solve Max (S, b)-fire, b 1, in k-caterpillars, a subclass of trees. Our paper is organized as follows. Definitions, terminology and preliminaries are given in Section 2. In Section 3 we establish a dichotomy on the complexity of (S, b)-fire and show that the greedy strategy mentioned above gives no approximation guarantee. In Section 4 we show that Max (S, b)-fire is polynomial-time solvable for k-caterpillars. Some variants of the Max (S, b)-fire problem are considered in Section 5. Conclusions are given in Section Preliminaries All graphs in this paper are undirected, connected, finite and simple. Let G = (V, E) be a graph. An edge in E between vertices u, v V will be denoted by uv. The degree of a vertex u V, denoted by deg(v), is the number of edges incident to u. We write G v for the subgraph obtained by deleting a vertex v and all the edges incident to v. Similarly, for A V, we denote by G A the subgraph of G obtained by deleting the set A and all the edges incident to some vertex in A. In order to define the firefighter problem, we use an undirected graph G = (V, E) and notations of [2]. Each vertex in the graph can be in exactly one of the following states: burned, saved or vulnerable. A vertex is said to be burned if it is on fire. We call a vertex saved if it is either protected by a firefighter that is, the vertex cannot be burned in subsequent time steps or if all paths from any burned vertex to it contains at least one protected vertex. Any vertex which is neither saved nor burned is called vulnerable. At time step t = 0, all vertices are vulnerable, except vertex s, which is burned. At each time t > 0, at most b vertices can be protected by firefighters and any vulnerable vertex v which is adjacent to a burned vertex u becomes burned at time t + 1, unless it is protected at time step t. Burned and saved vertices remain burned and saved, respectively. Given a graph G = (V, E) and a vertex s initially on fire, a protection strategy is a set Φ V T where T = {1, 2,..., V }. We say that a vertex v is protected at time t T according to the protection strategy Φ if (v, t) Φ. A protection strategy is valid with respect to a budget b, if the following two conditions are satisfied: 1. if (v, t) Φ then v is not burned at time t; 2. let Φ t = {(v, t) Φ}; then Φ t b for t = 1,..., V. Thus at each time t > 0, if a vulnerable vertex v is adjacent to at least one burned vertex and (v, t) Φ, then v gets burned at time t + 1. We define in the following the problems we study. (S, b)-fire. Input: An undirected graph G = (V, E), a burned vertex s V, and a subset S V. Question: Is there a valid strategy Φ with respect to budget b such that all vertices from S are saved? Max (S, b)-fire.

3 C. Bazgan et al. / Discrete Applied Mathematics 161 (2013) Fig. 1. T (r, 2, 3). Input: An undirected graph G = (V, E), a burned vertex s V, and a subset S V. Output: A valid strategy Φ with respect to budget b which maximizes the number of saved vertices that belong to S. In the figures of the paper, the burned vertices are represented by black vertices and the vertices from S are represented by. A protected vertex is represented by. Notice that the NP-hardness of (S, b)-fire implies the NP-hardness of Max (S, b)-fire since (S, b)-fire is a particular case of the decision version of Max (S, b)-fire. Furthermore, if Max (S, b)-fire is solvable in polynomial-time then so is (S, b)-fire. 3. Trees It has been shown in [7] that (S, 1)-Fire is NP-complete for trees of maximum degree three using a reduction from notall-equal 3SAT. Furthermore, (S, 1)-Fire is polynomial-time solvable for graphs of maximum degree three if the fire breaks out at a vertex of maximum degree two. In this section we generalize these results for any fixed b 2. First of all, we need to define some notions. Let T be a tree and let s be the vertex which is initially burned. From now on, s will be considered as the root of T. We define the level k of T to be the set of vertices that are at distance exactly k from s. The height of T is the length of a longest path from s to a leaf. An ancestor (resp. descendant) of a vertex v in T is any vertex on the path from s to v (resp. from v to a leaf which does not contain any ancestors of v). A child of a vertex v in T is an adjacent descendant of v. The tree T is said to be complete if every non-leaf vertex has exactly the same number of children. Remark 1. Without loss of generality, we may assume that strategies do not protect a vertex that has a protected ancestor in a tree. Remark 2. For (S, b)-fire on trees, we may assume without loss of generality that S is the set of leaves. Otherwise for each non-leaf vertex v S, since we have to save v, we can remove the subtree rooted at v such that v becomes a leaf. We denote by T (r, h, d) a complete tree of height h and root r such that every non-leaf vertex has exactly d children and every leaf is at the same distance from the root (see Fig. 1). For such a tree we obtain the following property. Lemma 1. Let b be the number of available firefighters at each time step. Consider a complete tree T (r, h, b+1). If the fire breaks out at r, then at least one leaf will not be saved. Proof. Since each non-leaf vertex has exactly b + 1 children, it follows that at each time step there will be at least one new burning vertex. Thus at the end of the process, at least one leaf will be burned. We also give the following preliminary results. Lemma 2. Among the strategies that save all the leaves (resp. a maximum number of a given subset of vertices) of a tree, there exists one such that each protected vertex is adjacent to a burning vertex. Proof. This is a straightforward adaptation of observation 4.1 in [14]. Lemma 3. Let T be a tree and let Φ be a strategy that saves all the leaves of T using at most b firefighters at each time step. Suppose there exist levels k and k > k containing b k b 1 and b k 1 firefighters, respectively. Then there exists a strategy Φ saving all the leaves of T and such that levels k and k contain b k + 1 and b k 1 firefighters, respectively. Proof. Let v k be a protected vertex by strategy Φ at a level k > k, and let v k be the ancestor of v k at level k. It follows from Remark 1 that we may assume that v k is not protected. We transform strategy Φ into a strategy Φ as follows (see Fig. 2): protect v k at time step k and do not protect v k at time step k, that is Φ = (Φ {(v k, k )}) {(v k, k)}. Since v k is an ancestor of v k, it follows that using strategy Φ, we save a subset of vertices that contains the vertices saved by using Φ. Since level k contains at most b 1 firefighters it follows that Φ is a valid strategy that saves all the leaves of T and levels k and k contain respectively b k + 1 and b k 1 firefighters. We now give the main result of this section.

5 C. Bazgan et al. / Discrete Applied Mathematics 161 (2013) Fig. 4. Construction of T from the tree T for the case b = 1. this argument iteratively for i 2,..., i B T, we obtain a strategy Φ that saves all the vertices in S and such that for any level k, b T (k) b. Thus, the strategy Φ restricted to the tree T is a valid strategy that saves all the leaves of T. Theorem 1 implies that Max (S, b)-fire is NP-hard for trees of maximum degree b + 2 when S is the set of leaves. Notice that Theorem 1 does not imply that Max (S, b)-fire is NP-hard when S is the set of all vertices. However, the following theorem shows that this is indeed the case. Proposition 1. For any fixed b 2, Max (S, b)-fire is NP-hard for trees of maximum degree b+3 when S is the set of all vertices. Proof. We construct a polynomial-time reduction from (S, b)-fire to Max (S, b)-fire where b 2. Let I be an instance of (S, b)-fire consisting of a tree T = (V, E) of maximum degree b+2 with V = n, a burned vertex s V, and a subset S V which corresponds to the set of leaves. We construct an instance I of Max (S, b)-fire consisting of a tree T = (V, E ), a set S = V, and a positive integer k as follows (see Fig. 4). For every leaf l of T, add b + 2 copies T 1,l,..., T b+2,l of the tree T (r, log b+1 n + 1, b + 1) such that the root r i,l of T i,l is adjacent to l, for i {1,..., b + 2}. Let T denote the cardinality of each of those trees. Notice that each tree T i,l has T n vertices. Set k = (b + 2) S T. We will prove that there exists a strategy for I that saves all the vertices in S if and only if there exists a strategy for I that saves at least k vertices in S. Suppose there exists a strategy Φ for I that saves all the vertices in S. Since S is the set of all leaves in T, it follows that the strategy Φ applied to T saves all the vertices of the trees T i,l. Notice that we have (b + 2) S such trees. Thus Φ saves at least k = (b + 2) S T vertices in T. Conversely, suppose that no strategy Φ for I can save all the vertices in S. Thus, at least one leaf of T is burned at the end. This necessarily implies that for any strategy Φ for I there is at least one vertex, say l, of S which is burned. It follows from the construction of T that in this case there are at least T vertices which will be burned for strategy Φ. Thus Φ saves at most n 1 + (b + 2) S T T (b + 2) S T 1 < k vertices. The following proposition shows that the sharp separation between the NP-hardness and polynomiality of (S, b)-fire on trees pointed out in [13] is preserved for any fixed b 2. Proposition 2. Let b 2 be any fixed integer and T a tree of maximum degree b + 2. If the fire breaks out at a vertex of degree at most b + 1 then all the leaves of T can be saved if and only if T is not complete. Thus (S, b)-fire is polynomial-time solvable for trees of maximum degree b + 2 if the fire breaks out at a vertex of degree at most b + 1. Proof. Notice that in this case we protect the vertices such that there is at most one new burning vertex v at each time step. Moreover, the fire stops when the vertex v has degree at most b + 1. Suppose that T is not complete. Then there exists a non-leaf vertex v of degree at most b + 1. From the previous remark we can direct the fire from s to v and stop it. Hence all the leaves of T are saved. Suppose that T is complete. Then at each time step, there is at least one new burning vertex. Thus there will be a leaf which will burn at the end of the process. Clearly, verifying whether a tree is complete can be done in polynomial time. Remark 3. Notice that Proposition 2 also holds for Max (S, b)-fire. Given a subset S of vertices, we direct the fire to a vertex of degree at most b + 1 such that the number of burned vertices in S is minimum. In [8], the authors asked whether there exists a constant c > 1 such that the degree greedy algorithm that consists, at each time step, to protect a highest degree vertex adjacent to a burning vertex, gives a polynomial-time c-approximation for Max (S, 1)-Fire for trees. The following proposition answers this question in the case when b firefighters are available at each time step for any b 1. Proposition 3. For any b 1, there exists no function f : N (1, + ) such that the degree greedy algorithm is an f (n)- approximation algorithm for Max (S, b)-fire for trees where S is the set of all vertices.

6 904 C. Bazgan et al. / Discrete Applied Mathematics 161 (2013) Fig. 5. Instance where the degree greedy algorithm gives no approximation guarantee for the case b = 1. Since here S = V, we did not represent vertices in S by squares. Fig. 6. A 3-star (left) and a 2-caterpillar (right). Fig. 7. A k-caterpillar for which the degree greedy algorithm (left) does not give the optimal solution (right). Since here S = V, we did not represent vertices in S by squares. Proof. Consider a tree T (r, h 1, b + 1) where h is a positive integer. Add a vertex s adjacent to r and a vertex v 1 adjacent to s; for i = 2,..., h 1, add a path of length i 1 with endpoints u i and v i such that u i is adjacent to s; finally, for i = 1,..., h 1, add b + 2 vertices adjacent to v i (see Fig. 5). Notice that the degree greedy algorithm protects vertices in the following order: v 1,..., v h 1. Thus it saves g h = (h 1) (b + 2) vertices. However, it is not difficult to see that the optimal solution protects vertices in the following order: r, v 2,..., v h 1. Thus, in an optimal solution we save opt h = (h 2)(b + 2) + h 1 i=0 (b + 1)i vertices. Since opt h g h + when h +, the result follows. 4. k-caterpillars In this section, we will present a subclass of trees for which Max (S, b)-fire is polynomial-time solvable for b 1. A caterpillar is a tree T such that the vertices of T with degree at least 2 induce a path. In other words, a caterpillar T consists of a path P such that all edges have at least one endpoint in P. A k-caterpillar, k 1, is a caterpillar in which any pending edge uv, with u V(P), v V(P), (i.e., any edge with exactly one endpoint in P) may be replaced by a path of length at most k (see Fig. 6). This path is then called a leg of the k-caterpillar at vertex u. Thus a caterpillar is a 1-caterpillar. A star is a tree consisting of one vertex, called the center of the star, adjacent to all the others. Thus a star on n vertices is isomorphic to the complete bipartite graph K 1,n 1. A k-star, k 1, is a tree obtained from a star in which any edge may be replaced by a path of length at most k (see Fig. 6). Thus a star is a 1-star. Notice that a k-star is a special case of a k-caterpillar In [14], the authors showed that the degree greedy algorithm gives an optimal solution for Max (S, 1)-Fire on caterpillars when S = V. However, this result does not hold for k-caterpillars, see for instance Fig. 7. In this section, we give a polynomial-time algorithm for Max (S, b)-fire for k-caterpillars for any b 1 and S V. In order to prove our main result of this section we first need to show the following. Theorem 2. For any k 1, b 1, Max (S, b)-fire is polynomial-time solvable for k-stars. Proof. We construct a polynomial-time reduction from Max (S, b)-fire to the Min Cost Flow problem which is known to be polynomial-time solvable (see for instance [1]). Let G = (V, E) be a k-star. First assume that s V is the center of G. Let

7 C. Bazgan et al. / Discrete Applied Mathematics 161 (2013) Fig. 8. The auxiliary digraph G. d = deg(s). Let P 1 = {sv 11, v 11 v 21,..., v (p1 1)1v p1 1},..., P d = {sv 1d, v 1d v 2d,..., v (pd 1)dv pd d} be the maximal paths of G starting at vertex s, with p 1,..., p d k and v 0j = s, for j = 1,..., d, if it exists. Let p = max{p 1,..., p d }. For each vertex v ij in these paths, we define S ij = {v ij, v (i+1)j,..., v pj j} S. Notice that we may assume that every path P j contains at least one vertex of S (otherwise we may delete V(P j ) \ {s}). We construct an auxiliary digraph G = (V, U ) (see Fig. 8), where V = {L 1,..., L p } {C 1,..., C d } {l, r} and U = {(L i, C j ) v ij P j } {(l, L i ) i = 1,..., p} {(C j, r) j = 1,..., d}. In this digraph G, we associate with each arc (L i, C j ), a cost u(i, j) = S ij. All other arcs have cost zero. Furthermore we associate with each arc (l, L i ) a capacity c(l, i) = b, with each arc (L i, C j ) a capacity c(i, j) = 1 and with each arc (C j, r) a capacity c(j, r) = 1. Finally we associate a supply of value d with vertex l and a demand of value d with vertex r (all other vertices have a supply and a demand equal to zero). Thus we obtain an instance of Min Cost Flow (we want to satisfy the supply and demand of each vertex with a minimum total cost and such that the capacity constraints are respected) and clearly G can be obtained from G in polynomial time. We show now that solving Max (S, b)-fire in G is equivalent to solving Min Cost Flow in G. Consider a feasible solution of Max (S, b)-fire in G of value ν. We may assume without loss of generality (see Remark 1) that at most one vertex is protected in each path P j, j {1,..., d}, and (see Lemma 2) that at most b vertices are protected in each set V i = {v i1, v i,2,..., v id }, i {1,..., p} (notice that some of these vertices v ij, j = 1,..., d, may not exist in G). Let D = {v ij v ij is protected, i {1,..., p}, j {1,..., d}}. Thus ν = v ij D S ij. Consider now some vertex v ij D. Then in G, we will use one flow unit on the path l-l i -C j -r. Repeating this procedure for every vertex in D, we obtain a flow in G of value D and of cost v ij D u(i, j) = v ij D S ij = ν. Since at most b vertices are protected in each set V i, it follows that at most b units of flow use the arc (l, L i ), for i {1,..., p}. Furthermore, since exactly one vertex is protected in each path P j, it follows that exactly one flow unit uses the arc (C j, r) for j {1,..., d}. Hence, we obtain a feasible solution of Min Cost Flow in G. Conversely, consider now a feasible solution of Min Cost Flow in G of value µ. Let A be the set of arcs (L i, C j ) used by a flow unit, i {1,..., p}, j {1,..., d}. Thus µ = (L i,c j ) A S ij. For each flow unit on a path l-l i -C j -r, we choose vertex v ij in G to be protected, for i {1,..., p}, j {1,..., d}. Since the capacity of an arc (l, L i ) is b, at most b vertices in V i will be chosen to be protected, i {1,..., p}. Let us denote by V i the set of vertices in V i chosen to be protected. Furthermore, since the capacity of an arc (C j, r) is one, exactly one vertex in each path P j will be chosen to be protected, j {1,..., d}. Thus, if we protect at each time step i the vertices in V i, we obtain a feasible solution of Max (S, b)-fire in G of value i S ij = µ. v ij V i Finally, we have to consider the case when s is not the center of G. The case when s has degree one is trivial. Thus we may assume now that deg(s) = 2. If b 2, we are done. Thus we may assume now that b = 1. If both neighbors of s are in S, then the optimal solution is clearly S 1. If both neighbors of s are not in S, then the optimal solution is clearly S. Hence the only case remaining is when exactly one neighbor of s is in S. Let u 1, u 2 be the neighbors of s such that u 1 S, u 2 S. If u 2 is not the center of G, the optimal solution is clearly S. Thus we may assume now that u 2 is the center of G. Let Q denote the set of vertices of the unique maximal path starting at vertex u 2 and containing u 1. In that case we have to compare the value of two solutions: (i) S 1 which is the value of the solution obtained by protecting first u 2 and then, during the second time step, we protect the neighbor of u 1 which is not s (if it exists); and (ii) the value of the solution obtained by protecting first u 1 and then applying our algorithm described above to the graph G (Q \ {u 2 }) (i.e., by reducing our problem to a Min Cost Flow problem). Remark 4. Notice that the polynomial reduction from Max (S, b)-fire to Min Cost Flow described in the proof of Theorem 2 is still valid if the number of vertices that can be protected at each time step is not constant (for instance, if we are allowed to protect at most b 1 vertices during the first time step, b 2 vertices during the second time step, etc.). In that case we just need to adapt the capacity of the arcs (l, L i ) accordingly.

8 906 C. Bazgan et al. / Discrete Applied Mathematics 161 (2013) Fig. 9. The construction of G with i = 5, j = 2, and q = 7. Furthermore the polynomial reduction remains valid in the case where some of the vertices in a set V i are not allowed to be protected during time step i. In this case we simply do not put an arc from L i to the corresponding vertices C j in G. Consider now a k-caterpillar G = (V, E). Let P be the path in the caterpillar from which G has been obtained, which is induced by vertices of degree at least two. We will call P the spine of the k-caterpillar. We are now ready to prove the main result of this section. Theorem 3. For any k 1, b 1, Max (S, b)-fire is polynomial-time solvable for k-caterpillars. Proof. Let G = (V, E) be a k-caterpillar and let P = {v 1 v 2, v 2 v 3,..., v p 1 v p } be the spine of G. First assume that s is a vertex of P, say s = v i, i {1,..., p}. Let P 1 = {v 1 v 2,..., v i 2 v i 1 } and P 2 = {v i+1 v i+2,..., v p 1 v p }. It follows from Remark 1 that we may assume that at most one vertex is protected in P 1 and at most one vertex is protected in P 2. Consider a strategy in which we decide to protect exactly two vertices of P, say vertex v j, for j {1,..., i 1} and vertex v q, for q {i + 1,..., p}. We may assume that v j is protected during time step i j and vertex v q is protected during time step q i (see Lemma 2). Notice that the vertices v j+1,..., v i 1, v i+1,..., v q 1 will not be protected in this strategy. Construct a (k + p)-star G as follows (see Fig. 9): (a) delete all vertices v 1,..., v j as well as the legs at these vertices (all these vertices are saved in our strategy); (b) delete all vertices v q,..., v p as well as the legs at these vertices (all these vertices are saved in our strategy); (c) delete all edges of P; (d) for every r {j + 1,..., i 1, i + 1,..., q 1}, let u r 1,..., ur deg(v r ) 2 be the neighbors of v r not belonging to P; delete v r and replace it by deg(v r ) 2 vertices v r 1,..., vr deg(v r ) 2 such that vr l is adjacent to u r l for l {1,..., deg(v r ) 2}; (e) for every r {j + 1,..., i 1} and l {1,..., deg(v r ) 2} create a path P rl of length i r with end points v i and v r l ; (f) for every r {i + 1,..., q 1} and l {1,..., deg(v r ) 2} create a path P rl of length r i with end points v i and v r l. From the above construction it follows that G is a (k+p)-star with center v i. Now in order to solve our initial problem, we need to solve Max (S, b)-fire in G with the following additional constraints: for every r {j+1,..., i 1, i+1,..., q 1} and every l {1,..., deg(v r ) 2} we are not allowed to protect the vertices of V(P rl ). Indeed, since we decided to protect v j and v q, the vertices v j+1,..., v i 1, v i+1,..., v q 1 will not be saved. Notice that these vertices are represented by the vertices of paths P rl in G. Moreover, if i j q j then at time steps i j and q j only b 1 firefighters are available (since we protect v j and v q at these time steps); if i j = q j then only b 2 firefighters are available at time step i j. It follows from Theorem 2 and Remark 4 that this problem can be solved in polynomial time. Since the number of choices of a pair of vertices (v j, v q ) to be protected on P is (i 1) (p i), we can determine in polynomial time the best strategy to adopt if we want to protect exactly two vertices on P. Notice that a similar procedure to the one described above can be used if we decide to protect exactly one vertex on P respectively if we decide not to protect any vertex of P. Clearly the number of choices of exactly one vertex v j, j {1,..., i 1, i + 1,..., p}, to be protected on P is p 1. Thus we conclude that if s V(P) we can determine an optimal strategy in polynomial time. It remains the case when s V(P). Similar to the proof of Theorem 2, we will distinguish several cases. The case when s has degree one is trivial. Thus we may assume now that deg(s) = 2. If b 2, we are done. Thus we may assume now that b = 1. If both neighbors of s are in S, then the optimal solution is clearly S 1. If both neighbors of s are not in S, then the optimal solution is clearly S. Hence the only case remaining is when exactly one neighbor of s is in S. Let u 1, u 2 be the neighbors of s such that u 1 S, u 2 S. If u 2 V(P), the optimal solution is clearly S. Thus we may assume now that u 2 V(P). In this case we have to compare the value of two solutions: (i) S 1 which is the value of the solution obtained by protecting first u 2 and then, during the second time step, we protect the neighbor of u 1 which is not s (if it exists); and (ii) the value of the solution obtained by protecting first u 1 and then applying our algorithm described above to the graph G (Q \ {u 2 }), where Q is the set of vertices of the unique maximal path starting at u 2 and containing u Variants of Max(S, b)-fire In this section, we give some results for a weighted version of Max (S, b)-fire as well as for its complementary version.

9 C. Bazgan et al. / Discrete Applied Mathematics 161 (2013) Weighted version We would like to mention that our positive results (Proposition 2, Theorem 2, and Theorem 3) may be generalized to a weighted version of Max (S, b)-fire. Suppose that we are given a weight w(v) for each vertex v S V. These weights may for instance reflect the importance of the vertices: if w(v 1 ) > w(v 2 ), vertex v 1 is considered as more important than vertex v 2. Then we may define the following problem: Max Weighted (S, b)-fire. Input: An undirected graph G = (V, E), a burned vertex s V, a subset S V, and a weight function w : S N. Output: A valid strategy Φ with respect to budget b which maximizes the total weight of the saved vertices that belong to S. Using the strategy in the proof of Proposition 2, if we direct the fire to a vertex of degree at most b + 1 such that the total weight of the burned vertices in S is minimum then we get the following result. Proposition 4. For any b 1, Max Weighted (S, b)-fire is polynomial-time solvable for trees of maximum degree b + 2 if the fire breaks out at a vertex of degree at most b + 1. Now by replacing the costs u(i, j) in the proof of Theorem 2 by u(i, j) = v S ij w(v) and adapting the case when s is not the center of G according to the weights, it is not difficult to see that we obtain the following. Theorem 4. For any k 1, b 1, Max Weighted (S, b)-fire is polynomial-time solvable for k-stars. Using this result and adapting the case when s V(P) according to the weights, it is straightforward that we obtain the following result. Theorem 5. For any k 1, b 1, Max Weighted (S, b)-fire is polynomial-time solvable for k-caterpillars. Although the results above are more general than the results in Sections 3 and 4, we decided to present in detail the results concerning Max (S, b)-fire in this paper, since this corresponds to the version which has been widely studied in the literature Min version Let us consider now the minimization version of the Max (S, b)-fire problem which is defined as follows. Min (S, b)-fire. Input: An undirected graph G = (V, E), a burned vertex s V, and a subset S V. Output: A valid strategy Φ with respect to budget b which minimizes the number of burned vertices that belong to S. In contrast to Max (S, b)-fire which is constant approximable on trees, the following theorem shows a strong inapproximability result for Min (S, b)-fire even when restricted to trees. Theorem 6. For any ϵ (0, 1) and any b 1, Min (S, b)-fire is not n 1 ϵ -approximable even for trees on n vertices when S is the set of all vertices, unless P = NP. Proof. We construct a polynomial-time reduction from (S, b)-fire to Min (S, b)-fire. Let I be an instance of (S, b)-fire consisting of a tree T = (V, E) with V = n 1, a burned vertex s V, and a subset S V which corresponds to the set of leaves. We construct an instance I of Min (S, b)-fire consisting of a tree T = (V, E ) with V = n, a burned vertex s, and S = V as follows. For every leaf l of T, add n β + b 1 vertices adjacent to l where β = 4 3. Notice that ϵ n = n β + b S + 1 n 1 < n β+3 1. If there exists a strategy that saves all the vertices in S then at most n 1 vertices are burned in V. Conversely, if there is no strategy that saves all the vertices in S then at least n β 1 vertices are burned in V. Suppose that there exists a polynomial-time n 1 ϵ -approximation algorithm A for Min (S, b)-fire. Thus, if I is a yesinstance, the algorithm gives a solution of value A(I ) n 1 ϵ n 1 < n (β+3)(1 ϵ)+1 1 = n β 1. If I is a no-instance, the solution value is A(I ) n β 1. Hence, the approximation algorithm A can distinguish in polynomial time between yes-instances and no-instances for (S, b)-fire implying that P = NP. 6. Conclusion In this paper, we studied some generalizations and variants of the firefighter problem when more than one firefighter is available at each time step and we answered three open questions of [8]. Several interesting questions remain open. The complexity of (S, b)-fire and Max (S, b)-fire in the following cases are not known: when the number of firefighters at each time step depends on the number of vertices; when every leaf is at the same level. The complexity of Max (S, b)-fire for trees of maximum degree b + 2 is not established. Finally, the problem is 2-approximable for trees when S is the set of vertices. Establishing non-approximability results or better approximability results is another open problem.

10 908 C. Bazgan et al. / Discrete Applied Mathematics 161 (2013) References [1] R. Ahuja, M. Thomas, O. James, Network Flows: Theory, Algorithms, and Applications, Prentice Hall, [2] E. Anshelevich, D. Chakrabarty, A. Hate, C. Swamy, Approximation algorithms for the firefighter problem: cuts over time and submodularity, in: Proceedings of the 20th International Symposium on Algorithms and Computation, ISAAC 09, in: LNCS, vol. 5878, 2009, pp [3] L. Cai, E. Verbin, L. Yang, Firefighting on trees: (1 1/e) approximation, fixed parameter tractability and a subexponential algorithm, in: Proceedings of the 19th International Symposium on Algorithms and Computation, ISAAC 08, in: LNCS, vol. 5369, 2008, pp [4] N. Chen, On the approximability of influence in social networks, in: Proceedings of the 19th Annual ACM SIAM Symposium on Discrete Algorithms, SODA 08, 2008, pp [5] M. Develin, S.G. Hartke, Fire containment in grids of dimension three and higher, Discrete Applied Mathematics 155 (17) (2007) [6] P.A. Dreyer, F.S. Roberts, Irreversible k-threshold processes: graph-theoretical threshold models of the spread of disease and of opinion, Discrete Applied Mathematics 157 (7) (2009) [7] S. Finbow, A. King, G. MacGillivray, R. Rizzi, The firefighter problem for graphs of maximum degree three, Discrete Mathematics 307 (16) (2007) [8] S. Finbow, G. MacGillivray, The firefighter problem: a survey of results, directions and questions, The Australasian Journal of Combinatorics 43 (2009) [9] B. Hartnell, Firefighter! an application of domination, presentation, in: 10th Conference on Numerical Mathematics and Computing, University of Manitoba in Winnipeg, Canada, [10] B. Hartnell, Q. Li, Firefighting on trees: how bad is the greedy algorithm? Congressus Numerantium 145 (2000) [11] Y. Iwaikawa, N. Kamiyama, T. Matsui, Improved approximation algorithms for firefighter problem on trees, IEICE Transactions on Information and Systems E94.D (2) (2011) [12] D. Kempe, J. Kleinberg, É Tardos, Maximizing the spread of influence through a social network, in: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 03, 2003, pp [13] A. King, G. MacGillivray, The firefighter problem for cubic graphs, Discrete Mathematics 310 (3) (2010) [14] G. MacGillivray, P. Wang, On the firefighter problem, Journal of Combinatorial Mathematics and Combinatorial Computing 47 (2003) [15] K.L. Ng, P. Raff, A generalization of the firefighter problem on Z Z, Discrete Applied Mathematics 156 (5) (2008) [16] A.E. Scott, U. Stege, N. Zeh, Politicians firefighting, in: Proceedings of the 17th International Symposium on Algorithms and Computation, ISAAC 06, in: LNCS, vol. 4288, 2006, pp

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