# Minimum Bisection is NP-hard on Unit Disk Graphs

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 Minimum Bisection is NP-hard on Unit Disk Graphs Josep Díaz 1 and George B. Mertzios 2 1 Departament de Llenguatges i Sistemes Informátics, Universitat Politécnica de Catalunya, Spain. 2 School of Engineering and Computing Sciences, Durham University, UK. Abstract. In this paper we prove that the Min-Bisection problem is NP-hard on unit disk graphs, thus solving a longstanding open question. Keywords: Minimum bisection problem, unit disk graphs, planar graphs, NP-hardness. 1 Introduction The problem of appropriately partitioning the vertices of a given graph into subsets, such that certain conditions are fulfilled, is a fundamental algorithmic problem. Apart from their evident theoretical interest, graph partitioning problems have great practical relevance in a wide spectrum of applications, such as in computer vision, image processing, and VLSI layout design, among others, as they appear in many divide-and-conquer algorithms (for an overview see [2]). In particular, the problem of partitioning a graph into equal sized components, while minimizing the number of edges among the components turns out to be very important in parallel computing. For instance, to parallelize applications we usually need to evenly distribute the computational load to processors, while minimizing the communication between processors. Given a simple graph G = (V, E) and k 2, a balanced k-partition of G = (V, E) is a partition of V into k vertex sets V 1, V 2,..., V k such that V i V k for every i = 1, 2,..., k. The cut size (or simply, the size) of a balanced k- partition is the number of edges of G with one endpoint in a set V i and the other endpoint in a set V j, where i j. In particular, for k = 2, a balanced 2-partition of G is also termed a bisection of G. The minimum bisection problem (or simply, Min-Bisection) is the problem, given a graph G, to compute a bisection of G with the minimum possible size, also known as the bisection width of G. Due to the practical importance of Min-Bisection, several heuristics and exact algorithms have been developed, which are quite efficient in practice [2], from the first ones in the 70 s [16] up to the very efficient one described in [7]. However, from the theoretical viewpoint, Min-Bisection has been one of the Partially supported by the EPSRC Grant EP/K022660/1.

2 most intriguing problems in algorithmic graph theory so far. This problem is well known to be NP-hard for general graphs [11], while it remains NP-hard when restricted to the class of everywhere dense graphs [18] (i.e. graphs with minimum degree Ω(n)), to the class of bounded maximum degree graphs [18], or to the class of d-regular graphs [5]. On the positive side, very recently it has been proved that Min-Bisection is fixed parameter tractable [6], while the currently best known approximation ratio is O(log n) [20]. Furthermore, it is known that Min-Bisection can be solved in polynomial time on trees and hypercubes [9, 18], on graphs with bounded treewidth [13], as well as on grid graphs with a constant number of holes [10, 19]. In spite of this, the complexity status of Min-Bisection on planar graphs, on grid graphs with an arbitrary number of holes, and on unit disk graphs have remained longstanding open problems so far [8,10,14,15]. The first two of these problems are equivalent, as there exists a polynomial time reduction from planar graphs to grid graphs with holes [19]. Furthermore, there exists a polynomial time reduction from planar graphs with maximum degree 4 to unit disk graphs [8]. Therefore, since grid graphs with holes are planar graphs of maximum degree 4, there exists a polynomial reduction of Min-Bisection from planar graphs to unit disk graphs. Another motivation for studying Min-Bisection on unit disk graphs comes from the area of wireless communication networks [1, 3], as the bisection width determines the communication bandwidth of the network [12]. Our contribution. In this paper we resolve the complexity of Min-Bisection on unit disk graphs. In particular, we prove that this problem is NP-hard by providing a polynomial reduction from a variant of the maximum satisfiability problem, namely from the monotone Max-XOR(3) problem (also known as the monotone Max-2-XOR(3) problem). Consider a monotone XOR-boolean formula φ with variables x 1, x 2,..., x n, i.e. a boolean formula that is the conjunction of XOR-clauses of the form (x i x k ), where no variable is negated. If, in addition, every variable x i appears in exactly k XOR-clauses in φ, then φ is called a monotone XOR( k) formula. The monotone Max-XOR( k) problem is, given a monotone XOR(k) formula φ, to compute a truth assignment of the variables x 1, x 2,..., x n that XOR-satisfies the largest possible number of clauses of φ. Recall here that the clause (x i x k ) is XOR-satisfied by a truth assignment τ if and only if x i x k in τ. Given a monotone XOR(k) formula φ, we construct a unit disk graph H φ such that the truth assignments that XOR-satisfy the maximum number of clauses in φ correspond bijectively to the minimum bisections in H φ, thus proving that Min-Bisection is NP-hard on unit disk graphs. Organization of the paper. Necessary definitions and notation are given in Section 2. In Section 3, given a monotone XOR(3)-formula φ with n variables, we construct an auxiliary unit disk graph G n, which depends only on the size n of φ (and not on φ itself). In Section 4 we present our reduction from the monotone Max-XOR(3) problem to Min-Bisection on unit disk graphs, by modifying the graph G n to a unit disk graph H φ which also depends on the formula φ 2

3 itself. Finally we discuss the presented results and remaining open problems in Section 5. 2 Preliminaries and Notation We consider in this article simple undirected graphs with no loops or multiple edges. In an undirected graph G = (V, E), the edge between vertices u and v is denoted by uv, and in this case u and v are said to be adjacent in G. For every vertex u V the neighborhood of u is the set N(u) = {v V uv E} of its adjacent vertices and its closed neighborhood is N[u] = N(u) {u}. The subgraph of G that is induced by the vertex subset S V is denoted G[S]. Furthermore a vertex subset S V induces a clique in G if uv E for every pair u, v S. A graph G = (V, E) with n vertices is the intersection graph of a family F = {S 1,..., S n } of subsets of a set S if there exists a bijection µ : V F such that for any two distinct vertices u, v V, uv E if and only if µ(u) µ(v). Then, F is called an intersection model of G. A graph G is a disk graph if G is the intersection graph of a set of disks (i.e. circles together with their internal area) in the plane. A disk graph G is a unit disk graph if there exists a disk intersection model for G where all disks have equal radius (without loss of generality, all their radii are equal to 1). Given a disk (resp. unit disk) graph G, an intersection model of G with disks (resp. unit disks) in the plane is called a disk (resp. unit disk) representation of G. Alternatively, unit disk graphs can be defined as the graphs that can be represented by a set of points on the plane (where every point corresponds to a vertex) such that two vertices intersect if and only if the corresponding points lie at a distance at most some fixed constant c (for example c = 1). Although these two definitions of unit disk graphs are equivalent, in this paper we use the representation with the unit disks instead of the representation with the points. Note that any unit disk representation R of a unit disk graph G = (V, E) can be completely described by specifying the centers c v of the unit disks D v, where v V, while for any disk representation we also need to specify the radius r v of every disk D v, v V. Given a graph G, it is NP-hard to decide whether G is a disk (resp. unit disk) graph [4, 17]. Given a unit disk representation R of a unit disk graph G, in the remainder of the paper we may not distinguish for simplicity between a vertex of G and the corresponding unit disk in R, whenever it is clear from the context. It is well known that the Max-XOR problem is NPhard. Furthermore, it remains NP-hard even if the given formula φ is restricted to be a monotone XOR(3) formula. For the sake of completeness we provide in the next lemma a proof of this fact. Lemma 1. Monotone Max-XOR(3) is NP-hard. 3

4 3 Construction of the unit disk graph G n In this section we present the construction of the auxiliary unit disk graph G n, given a monotone XOR(3)-formula φ with n variables. Note that G n depends only on the size of the formula φ and not on φ itself. Using this auxiliary graph G n we will then construct in Section 4 the unit disk graph H φ, which depends also on φ itself, completing thus the NP-hardness reduction from monotone Max- XOR(3) to the minimum bisection problem on unit disk graphs. We define G n by providing a unit disk representation R n for it. For simplicity of the presentation of this construction, we first define a set of halflines on the plane, on which all centers of the disks are located in the representation R n. 3.1 The half-lines containing the disk centers Denote the variables of the formula φ by {x 1, x 2,..., x n }. Define for simplicity the values d 1 = 5.6 and d 2 = 7.2. For every variable x i, where i {1, 2,..., n}, we define the following four points in the plane: p i,0 = (2i d 1, 2(i 1) d 2 ) and p i,1 = ((2i 1) d 1, (2i 1) d 2 ), which are called the bend points for variable x i, and q i,0 = ((2i 1) d 1, 2(i 1) d 2 ) and r i,0 = (2i d 1, 2i d 2 ), which is called the auxiliary points for variable x i. Then, starting from point p i,j, where i {1, 2,..., n} and j {0, 1}, we draw in the plane one halfline parallel to the x-axis pointing to the left and one halfline parallel to the y-axis pointing upwards. The union of these two halflines on the plane is called the track T i,j of point p i,j. Note that, by definition of the points p i,j, the tracks T i,0 and T i,1 do not have any common point, and that, whenever i k, the tracks T i,j and T k,l have exactly one common point. Furthermore note that, for every i {1, 2,..., n}, both auxiliary points q i,0 and r i,0 belong to the track T i,0. We will construct the unit disk representation R n of the graph G n in such a way that the union of all tracks T i,j will contain the centers of all disks in R n.the construction of R n is done by repeatedly placing on the tracks T i,j multiple copies of three particular unit disk representations Q 1 (p), Q 2 (p), and Q 3 (p) (each of them including 2 +2 unit disks), which we use as gadgets in our construction. Before we define these gadgets we need to define first the notion of a (t, p)-crowd. Definition 1. Let ε > 0 be infinitesimally small. Let t 1 and p = (x p, y p ) be a point in the plane. Then, the horizontal (t, p)-crowd (resp. the vertical (t, p)- crowd) is a set of t unit disks whose centers are equally distributed between the points (x p ε, y p ) and (x p + ε, y p ) (resp. between the points (x p, y p ε) and (x p, y p + ε)). Note that, by Definition 1, both the horizontal and the vertical (t, p)-crowds represent a clique of t vertices. Furthermore note that both the horizontal and 4

5 the vertical (1, p)-crowds consist of a single unit disk centered at point p. For simplicity of the presentation, we will graphically depict in the following a (t, p)- crowd just by a disk with a dashed contour centered at point p, and having the number t written next to it. Furthermore, whenever the point p lies on the horizontal (resp. vertical) halfline of a track T i,j, then any (t, p)-crowd will be meant to be a horizontal (resp. vertical) (t, p)-crowd. 3.2 Three useful gadgets Let p = (p x, p y ) be a point on a track T i,j. Whenever p lies on the horizontal halfline of T i,j, we define for any δ > 0 (with a slight abuse of notation) the points p δ = (p x δ, p y ) and p + δ = (p x + δ, p y ). Similarly, whenever p lies on the vertical halfline of T i,j, we define for any δ > 0 the points p δ = (p x, p y δ) and p+δ = (p x, p y +δ). Assume first that p lies on the horizontal halfline of T i,j. Then we define the unit disk representation Q 1 (p) as follows: Q 1 (p) consists of the horizontal (n 3, p + 0.9)-crowd, the horizontal (2 2n 3 + 2, p + 2.8)-crowd, and the horizontal (n 3, p + 4.7)-crowd, as it is illustrated in Figure 1(a). Assume now that p lies on the vertical halfline of T i,j, we define the unit disk representations Q 2 (p) and Q 3 (p) as follows: Q 2 (p) consists of a single unit disk centered at point p, the vertical (, p + 1.8)-crowd, a single unit disk centered at point p + 3.6, and the vertical (, p + 5.4)-crowd, as it is illustrated in Figure 1(b). Q 3 (p) consists of a single unit disk centered at point p, the vertical (, p + 1.7)-crowd, a single unit disk centered at point p + 3.6, and the vertical (, p + 5.4)-crowd, as it is illustrated in Figure 1(c). In the above definition of the unit disk representation Q k (p), where k {1, 2, 3}, the point p is called the origin of Q k (p). Note that the origin p of the representation Q 2 (p) (resp. Q 3 (p)) is a center of a unit disk in Q 2 (p) (resp. Q 3 (p)). In contrast, the origin p of the representation Q 1 (p) is not a center of any unit disk of Q 1 (p), however p lies in Q 1 (p) within the area of each of the n 3 unit disks of the horizontal (n 3, p + 0.9)-crowd of Q 1 (p). For every point p, each of Q 1 (p), Q 2 (p), and Q 3 (p) has in total unit disks (cf. Figure 1). Furthermore, for any i {1, 2, 3} and any two points p and p in the plane, the unit disk representation Q i (p ) is an isomorphic copy of the representation Q i (p), which is placed at the origin p instead of the origin p. Moreover, for any point p in the vertical halfline of a track T i,j, the unit disk representations Q 2 (p) and Q 3 (p) are almost identical: their only difference is that the vertical (, p + 1.8)-crowd in Q 2 (p) is replaced by the vertical (, p + 1.7)-crowd in Q 3 (p), i.e. this whole crowd is just moved downwards by 0.1 in Q 3 (p). Observation 1 Let k {1, 2, 3} and p T i,j, where i {1, 2,..., n} and j {0, 1}. For every two adjacent vertices u, v in the unit disk graph defined by Q k (p), u and v belong to a clique of size at least

6 Q 3 (p) : Q 2 (p) : Q 3 (p) : Q 1 (p) : n 3 2 2n n p p p (a) (b) (c) Fig. 1. The unit disk representations Q 1(p), Q 2(p), and Q 3(p), where p is a point on one of the tracks T i,j, where 1 i n and j {0, 1}. 3.3 The unit disk representation R n of G n We are now ready to iteratively construct the unit disk representation R n of the graph G n, using the above gadgets Q 1 (p), Q 2 (p), and Q 3 (p), as follows: (a) for every i {1, 2,..., n} and for every j {0, 1}, add to R n : the gadget Q 1 (p), with its origin at the point p = (0, (2(i 1) + j) d 2 ), (b) for every i {1, 2,..., n}, add to R n : the gadgets Q 1 (q i,0 ), Q 2 (r i,0 ), Q 3 (p i,0 ), and Q 3 (p i,1 ), the gadgets Q 1 (p) and Q 1 (p ), with their origin at the points p = ( d 1, (2i 1) d 2 ) and p = ( 2d 1, (2i 1) d 2 ) of the track T i,1, respectively, (c) for every i, k {1, 2,..., n} and for every j, l {0, 1}, where i k, add to R n : the gadgets Q 1 (p) and Q 2 (p), with their origin at the (unique) point p that lies on the intersection of the tracks T i,j and T k,l. This completes the construction of the unit disk representation R n of the graph G n = (V n, E n ), in which the centers of all unit disks lie on some track T i,j, where i {1, 2,..., n} and j {0, 1}. Definition 2. Let i {1, 2,..., n} and j {0, 1}. The vertex set S i,j V n consists of all vertices of those copies of the gadgets Q 1 (p), Q 2 (p), and Q 3 (p), whose origin p belongs to the track T i,j. 6

7 For every v V n let c v be the center of its unit disk in the representation R n. Note that, by Definition 2, the unique vertex v V n, for which c v T i,j T k,l, where i < k (i.e. c v lies on the intersection of the vertical halfline of T i,j with the horizontal halfline of T k,l ), we have that v S i,j. Furthermore note that {S i,j : 1 i n, j {0, 1}} is a partition of the vertex set V n of G n. In the next lemma we show that this is also a balanced 2n-partition of G n, i.e. S i,j = S k,l for every i, k {1, 2,..., n} and j, l {0, 1}. Lemma 2. For every i {1, 2,..., n} and j {0, 1}, we have that S i,j = 4(n + 1)( + 1). Consider the intersection point p of two tracks T i,j and T k,l, where i k. Assume without loss of generality that i < k, i.e. p belongs to the vertical halfline of T i,j and on the horizontal halfline of T k,l, cf. Figure 2(a). Then p is the origin of the gadget Q 2 (p) in the representation R n (cf. part (c) of the construction of R n ). Therefore p is the center of a unit disk in R n, i.e. p = c v for some v S i,j V n. All unit disks of R n that intersect with the disk centered at point p is shown in Figure 2(a). Furthermore, the induced subgraph G n [{v} N(v)] on the vertices of G n, which correspond to these disks of Figure 2(a), is shown in Figure 2(c). In Figure 2(c) we denote by K and K n 3 the cliques with and with n 3 vertices, respectively, and the thick edge connecting the two K n 3 s depicts the fact that all vertices of the two K n 3 s are adjacent to each other. Now consider a bend point p i,j of a variable x i, where j {0, 1}. Then p i,j is the origin of the gadget Q 3 (p i,j ) in the representation R n (cf. the first bullet of part (b) of the construction of R n ). Therefore p i,j is the center of a unit disk in R n, i.e. p = c v for some v S i,j V n. All unit disks of R n that intersect with the disk centered at point p i,j are shown in Figure 2(b). Furthermore, the induced subgraph G n [{v} N(v)] of G n that corresponds to the disks of Figure 2(b), is shown in Figure 2(d). In both Figures 2(a) and 2(b), the area of the intersection of two crowds (i.e. disks with dashed contour) is shaded gray for better visibility. Lemma 3. Consider an arbitrary bisection B of G n with size strictly less than. Then for every set S i,j, i {1, 2,..., n} and j {0, 1}, all vertices of S i,j belong to the same color class of B. 4 Minimum bisection on unit disk graphs In this section we provide our polynomial-time reduction from the monotone Max-XOR(3) problem to the minimum bisection problem on unit disk graphs. To this end, given a monotone XOR(3) formula φ with n variables and m = 3n 2 clauses, we appropriately modify the auxiliary unit disk graph G n of Section 3 to obtain the unit disk graph H φ. Then we prove that the truth assignments that satisfy the maximum number of clauses in φ correspond bijectively to the minimum bisections in H φ. We construct the unit disk graph H φ = (V φ, E φ ) from G n = (V n, E n ) as follows. Let (x i x k ) be a clause of φ, where i < k. Let p 0 (resp. p 1 ) be the 7

8 T i,j p = c v T i,j 1.8 n3 n n 3 p i,j = c v K p = c v K T k,l 1.8 T i,j K n 3 K n 3 K n 3 p i,j = c v 0.9 K (a) (b) (c) (d) Fig. 2. The disks in R n (a) around the intersection point p = c v of two tracks T i,j and T k,l, where i < k, and (b) around the bend point p i,j = c v of a variable x i, where j {0, 1}. (c) The induced subgraph of G n on the vertices of part (a), and (d) the induced subgraph of G n for part (b). unique point in the unit disk representation R n that lies on the intersection of the tracks T i,0 and T k,1 (resp. on the intersection of the tracks T i,1 and T k,0 ). For every point p {p 0, p 1 }, where we denote p = (p x, p y ), we modify the gadgets Q 1 (p) and Q 2 (p) in the representation R n as follows: (a) replace the horizontal (n 3, p + 0.9)-crowd of Q 1 (p) by the horizontal (n 3 1, p + 0.9)-crowd and a single unit disk centered at (p x +0.9, p y +0.02), (b) replace the vertical (, p + 1.8)-crowd of Q 2 (p) by the vertical ( 1, p + 1.8)-crowd and a single unit disk centered at (p x +0.02, p y +1.8). That is, for every point p {p 0, p 1 }, we first move one (arbitrary) unit disk of the horizontal (n 3, p + 0.9)-crowd of Q 1 (p) upwards by 0.02, and then we move one (arbitrary) unit disk of the vertical (, p + 1.8)-crowd of Q 2 (p) to the right by In the resulting unit disk representation these two unit disks intersect, whereas they do not intersect in the representation R n. Furthermore it is easy to check that for any other pair of unit disks, these disks intersect in the resulting representation if and only if they intersect in R n. Denote by R φ the unit disk representation that is obtained from R n by performing the above modifications for all clauses of the formula φ. Then H φ is the unit disk graph induced by R φ. Note that, by construction, the graphs H φ and G n have exactly the same vertex set, i.e. V φ = V n, and that E n E φ. In particular, note that the sets S i,j (cf. Definition 2) induce the same subgraphs in both H φ and G n, and thus the next corollary follows directly by Lemma 3. Corollary 1. Consider an arbitrary bisection B of H φ with size strictly less than. Then for every set S i,j, i {1, 2,..., n} and j {0, 1}, all vertices of S i,j belong to the same color class of B. 8

9 Theorem 1. There exists a truth assignment τ of the formula φ that satisfies at least k clauses if and only if the unit disk graph H φ has a bisection with value at most 2n 4 (n 1) + 3n 2k. Proof (sketch). The ( ) part of the proof is omitted due to lack of space. ( ) Assume that H φ has a minimum bisection B with value at most 2n 4 (n 1) + 3n 2k. Denote the two color classes of B by blue and red, respectively. Since the size of B is strictly less than, Corollary 1 implies that for every i {1, 2,..., n} and j {0, 1}, all vertices of the set S i,j belong to the same color class of B. Therefore, all cut edges of B have one endpoint in a set S i,j and the other endpoint in a set S k,l, where (i, j) (k, l). Furthermore, since B is a bisection of H φ, Lemma 2 implies that exactly n of the sets {S i,j : 1 i n, j {0, 1}} are colored blue and the other n ones are colored red in B. First we will prove that, for every i {1, 2,..., n}, the sets S i,0 and S i,1 belong to different color classes in B. To this end, let t 0 be the number of variables x i, 1 i n, for which both sets S i,0 and S i,1 are colored blue (such variables x i are called blue). Then, since B is a bisection of H φ, there must be also t variables x i, 1 i n, for which both sets S i,0 and S i,1 are colored red (such variables x i are called red), whereas n 2t variables x i, for which one of the sets {S i,0, S i,1 } is colored blue and the other one red (such variables x i are called balanced). Using the minimality of the bisection B, we will prove that t = 0. Every cut edge of B occurs at the intersection of the tracks of two variables x i, x k, where either both x i, x k are balanced variables, or one of them is a balanced and the other one is a blue or red variable, or one of them is a blue and the other one is a red variable. Furthermore recall by the construction of the graph H φ from the graph G n that every clause (x i x k ) of the formula φ corresponds to an intersection of the tracks of the variables x i and x k. Among the m clauses of φ, let m 1 of them correspond to intersections of tracks of two balanced variables, m 2 of them correspond to intersections of tracks of a balanced variable and a blue or red variable, and m 3 of them correspond to intersections of tracks of a blue variable and a red variable. Note that m 1 + m 2 + m 3 m. Let 1 i < k n. In the following we distinguish the three cases of the variables x i, x k that can cause a cut edge in the bisection B. x i and x k are both balanced variables: in total there are (n 2t)(n 2t 1) 2 such pairs of variables, where exactly m 1 of them correspond to a clause (x i x k ) of the formula φ. It is easy to check that, for every such pair x i, x k that does not correspond to a clause of φ, the intersection of the tracks of x i and x k contributes exactly 2n 3 + 2n 3 = 4n 3 edges to the value of B. Furthermore, for each of the m 1 other pairs x i, x k that correspond to a clause of φ, the intersection of the tracks of x i and x k contributes either 4n 3 or 4n edges to the value of B. In particular, if the vertices of the sets S i,0 and S k,1 have the same color in B then the pair x i, x k contributes 4n 3 edges to the value of B, otherwise it contributes 4n edges. Among these m 1 clauses, let m 1 of them contribute 4n 3 edges each and the remaining m 1 m 1 of them contribute 4n edges each. 9

10 one of x i, x k is a balanced variable and the other one is a blue or red variable: in total there are (n 2t)2t such pairs of variables, where exactly m 2 of them correspond to a clause (x i x k ) of the formula φ. It is easy to check that, for every such pair x i, x k that does not correspond to a clause of φ, the intersection of the tracks of x i and x k contributes exactly 2n 3 + 2n 3 = 4n 3 edges to the value of B. Furthermore, for each of the m 2 other pairs x i, x k that correspond to a clause of φ, the intersection of the tracks of x i and x k contributes 4n edges to the value of B. one of x i, x k is a blue variable and the other one is a red variable: in total there are t 2 such pairs of variables, where exactly m 3 of them correspond to a clause (x i x k ) of the formula φ. It is easy to check that, for every such pair x i, x k that does not correspond to a clause of φ, the intersection of the tracks of x i and x k contributes exactly 4 2n 3 = 8n 3 edges to the value of B. Furthermore, for each of the m 3 other pairs x i, x k that correspond to a clause of φ, the intersection of the tracks of x i and x k contributes 8n edges to the value of B. Therefore, the value of B can be computed (the exact details are omitted due to lack of space) as 2n 4 (n 1) + 4n 3 t + 2(m 1 m 1) + m 2 + 2m 3. Note now that 0 2(m 1 m 1) + m 2 + 2m 3 2m = 3n < 4n 3. Therefore, since the value of the bisection B is minimum by assumption, it follows that t = 0. Thus for every i {1, 2,..., n} the variable x i of φ is balanced in the bisection B, i.e. the sets S i,0 and S i,1 belong to different color classes in B. That is, m 1 = m and m 2 = m 3 = 0, and thus the value of B is equal to 2n 4 (n 1) + 2(m m 1). On the other hand, since the value of B is at most 2n 4 (n 1) + 3n 2k by assumption, it follows that 2(m m 1) 3n 2k. Therefore, since m = 3n 2, it follows that m 1 k. We define now from B the truth assignment τ of φ as follows. For every i {1, 2,..., n}, if the vertices of the set S i,0 are blue and the vertices of the set S i,1 are red in B, then we set x i = 0 in τ. Otherwise, if the vertices of the set S i,0 are red and the vertices of the set S i,1 are blue in B, then we set x i = 1 in τ. Recall that m 1 is the number of clauses of φ that contribute 4n 3 edges each to the value of B, while the remaining m m 1 clauses of φ contribute 4n edges each to the value of B. Thus, by the construction of H φ from G n, for every clause (x i x k ) of φ that contributes 4n 3 (resp. 4n 3 + 2) to the value of B, the vertices of the sets S i,0 and S k,1 have the same color (resp. S i,0 and S k,1 have different colors) in B. Therefore, by definition of the truth assignment τ, there are exactly m 1 clauses (x i x k ) of φ where x i x k in τ, and there are exactly m m 1 clauses (x i x k ) of φ where x i = x k in τ. That is, τ satisfies exactly m 1 k of the m clauses of φ. This completes the proof of the theorem. We can now state our main result, which follows by Theorem 1 and Lemma 1. Theorem 2. Min-Bisection is NP-hard on unit disk graphs. 10

11 5 Concluding Remarks In this paper we proved that Min-Bisection is NP-hard on unit disk graphs by providing a polynomial time reduction from the monotone Max-XOR(3) problem, thus solving a longstanding open question. As pointed out in the Introduction, our results indicate that Min-Bisection is probably also NP-hard on planar graphs, or equivalently on grid graphs with an arbitrary number of holes, which remains yet to be proved. References 1. I. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci. Wireless sensor networks: A survey. Computer Networks, 38: , C.-E. Bichot and P. Siarry, editors. Graph Partitioning. Wiley, M. Bradonjic, R. Elsässer, T. Friedrich, T. Sauerwald, and A. Stauffer. Efficient broadcast on random geometric graphs. In Proceedings of the 21st annual ACM- SIAM Symposium on Discrete Algorithms (SODA), pages , H. Breu and D. G. Kirkpatrick. Unit disk graph recognition is NP-hard. Computational Geometry, 9(1-2):3 24, T. Bui, S. Chaudhuri, T. Leighton, and M.Sipser. Graph bisection algorithms with good average case behavior. Combinatorica, 7:1987, M. Cygan, D. Lokshtanov, M. Pilipczuk, M. Pilipczuk, and S. Saurabh. Minimum bisection is fixed parameter tractable. In Proceedings of the 46th Annual Symposium on the Theory of Computing (STOC), To appear. 7. D. Delling, A. V. Goldberg, I. Razenshteyn, and R. F. Werneck. Exact combinatorial branch-and-bound for graph bisection. In Proceedings of the 14th Meeting on Algorithm Engineering & Experiments (ALENEX), pages 30 44, J. Díaz, M. D. Penrose, J. Petit, and M. J. Serna. Approximating layout problems on random geometric graphs. Journal of Algorithms, 39(1):78 116, J. Díaz, J. Petit, and M. Serna. A survey on graph layout problems. ACM Computing Surveys, 34: , A. E. Feldmann and P. Widmayer. An O(n 4 ) time algorithm to compute the bisection width of solid grid graphs. In Proceedings of the 19th annual European Symposium on Algorithms (ESA), pages , M. R. Garey and D. S. Johnson. Computers and intractability: A guide to the theory of NP-completeness. W. H. Freeman & Co., J. Hromkovic, R. Klasing, A. Pelc, P. Ruzicka, and W. Unger. Dissemination of Information in Communication Networks - Broadcasting, Gossiping, Leader Election, and Fault-Tolerance. Texts in Theoretical Computer Science. An EATCS Series. Springer, K. Jansen, M. Karpinski, A. Lingas, and E. Seidel. Polynomial time approximation schemes for Max-Bisection on planar and geometric graphs. SIAM Journal on Computing, 35(1): , S. Kahruman-Anderoglu. Optimization in geometric graphs: Complexity and approximation. PhD thesis, Texas A & M University, M. Karpinski. Approximability of the minimum bisection problem: An algorithmic challenge. In Proceedings of the 27th International Symposium on Mathematical Foundations of Computer Science (MFCS), pages 59 67,

12 16. B. Kernighan and S. Lin. An efficient heuristic procedure for partitioning graphs. Bell System Technical Journal, 49(2): , J. Kratochvíl. Intersection graphs of noncrossing arc-connected sets in the plane. In Proceedings of the 4th Int. Symp. on Graph Drawing (GD), pages , R. MacGregor. On partitioning a graph: A theoretical and empirical study. PhD thesis, University of California, Berkeley, C. H. Papadimitriou and M. Sideri. The bisection width of grid graphs. Mathematical Systems Theory, 29(2):97 110, H. Räcke. Optimal hierarchical decompositions for congestion minimization in networks. In Proceedings of the 40th Annual ACM Symposium on Theory of Computing (STOC), pages ,

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

Discrete Applied Mathematics 161 (2013) 899 908 Contents lists available at SciVerse ScienceDirect Discrete Applied Mathematics journal homepage: www.elsevier.com/locate/dam The firefighter problem with

### On the k-path cover problem for cacti

On the k-path cover problem for cacti Zemin Jin and Xueliang Li Center for Combinatorics and LPMC Nankai University Tianjin 300071, P.R. China zeminjin@eyou.com, x.li@eyou.com Abstract In this paper we

### Lecture 7: NP-Complete Problems

IAS/PCMI Summer Session 2000 Clay Mathematics Undergraduate Program Basic Course on Computational Complexity Lecture 7: NP-Complete Problems David Mix Barrington and Alexis Maciel July 25, 2000 1. Circuit

### Approximated Distributed Minimum Vertex Cover Algorithms for Bounded Degree Graphs

Approximated Distributed Minimum Vertex Cover Algorithms for Bounded Degree Graphs Yong Zhang 1.2, Francis Y.L. Chin 2, and Hing-Fung Ting 2 1 College of Mathematics and Computer Science, Hebei University,

### A Note on Maximum Independent Sets in Rectangle Intersection Graphs

A Note on Maximum Independent Sets in Rectangle Intersection Graphs Timothy M. Chan School of Computer Science University of Waterloo Waterloo, Ontario N2L 3G1, Canada tmchan@uwaterloo.ca September 12,

### Labeling outerplanar graphs with maximum degree three

Labeling outerplanar graphs with maximum degree three Xiangwen Li 1 and Sanming Zhou 2 1 Department of Mathematics Huazhong Normal University, Wuhan 430079, China 2 Department of Mathematics and Statistics

### Definition 11.1. Given a graph G on n vertices, we define the following quantities:

Lecture 11 The Lovász ϑ Function 11.1 Perfect graphs We begin with some background on perfect graphs. graphs. First, we define some quantities on Definition 11.1. Given a graph G on n vertices, we define

### Shortcut sets for plane Euclidean networks (Extended abstract) 1

Shortcut sets for plane Euclidean networks (Extended abstract) 1 J. Cáceres a D. Garijo b A. González b A. Márquez b M. L. Puertas a P. Ribeiro c a Departamento de Matemáticas, Universidad de Almería,

### Cycle transversals in bounded degree graphs

Electronic Notes in Discrete Mathematics 35 (2009) 189 195 www.elsevier.com/locate/endm Cycle transversals in bounded degree graphs M. Groshaus a,2,3 P. Hell b,3 S. Klein c,1,3 L. T. Nogueira d,1,3 F.

### Exponential time algorithms for graph coloring

Exponential time algorithms for graph coloring Uriel Feige Lecture notes, March 14, 2011 1 Introduction Let [n] denote the set {1,..., k}. A k-labeling of vertices of a graph G(V, E) is a function V [k].

### Generating models of a matched formula with a polynomial delay

Generating models of a matched formula with a polynomial delay Petr Savicky Institute of Computer Science, Academy of Sciences of Czech Republic, Pod Vodárenskou Věží 2, 182 07 Praha 8, Czech Republic

### Divide-and-Conquer. Three main steps : 1. divide; 2. conquer; 3. merge.

Divide-and-Conquer Three main steps : 1. divide; 2. conquer; 3. merge. 1 Let I denote the (sub)problem instance and S be its solution. The divide-and-conquer strategy can be described as follows. Procedure

### Competitive Analysis of On line Randomized Call Control in Cellular Networks

Competitive Analysis of On line Randomized Call Control in Cellular Networks Ioannis Caragiannis Christos Kaklamanis Evi Papaioannou Abstract In this paper we address an important communication issue arising

### Scheduling Shop Scheduling. Tim Nieberg

Scheduling Shop Scheduling Tim Nieberg Shop models: General Introduction Remark: Consider non preemptive problems with regular objectives Notation Shop Problems: m machines, n jobs 1,..., n operations

### Data Migration in Heterogeneous Storage Systems

011 31st International Conference on Distributed Computing Systems Data Migration in Heterogeneous Storage Systems Chadi Kari Department of Computer Science and Engineering University of Connecticut Storrs,

### Guessing Game: NP-Complete?

Guessing Game: NP-Complete? 1. LONGEST-PATH: Given a graph G = (V, E), does there exists a simple path of length at least k edges? YES 2. SHORTEST-PATH: Given a graph G = (V, E), does there exists a simple

### Removing Even Crossings

EuroComb 2005 DMTCS proc. AE, 2005, 105 110 Removing Even Crossings Michael J. Pelsmajer 1, Marcus Schaefer 2 and Daniel Štefankovič 2 1 Department of Applied Mathematics, Illinois Institute of Technology,

### Data Management in Hierarchical Bus Networks

Data Management in Hierarchical Bus Networks F. Meyer auf der Heide Department of Mathematics and Computer Science and Heinz Nixdorf Institute, Paderborn University, Germany fmadh@upb.de H. Räcke Department

### Diversity Coloring for Distributed Data Storage in Networks 1

Diversity Coloring for Distributed Data Storage in Networks 1 Anxiao (Andrew) Jiang and Jehoshua Bruck California Institute of Technology Pasadena, CA 9115, U.S.A. {jax, bruck}@paradise.caltech.edu Abstract

### Removing Local Extrema from Imprecise Terrains

Removing Local Extrema from Imprecise Terrains Chris Gray Frank Kammer Maarten Löffler Rodrigo I. Silveira Abstract In this paper we consider imprecise terrains, that is, triangulated terrains with a vertical

### Optimal Binary Space Partitions in the Plane

Optimal Binary Space Partitions in the Plane Mark de Berg and Amirali Khosravi TU Eindhoven, P.O. Box 513, 5600 MB Eindhoven, the Netherlands Abstract. An optimal bsp for a set S of disjoint line segments

### Small Maximal Independent Sets and Faster Exact Graph Coloring

Small Maximal Independent Sets and Faster Exact Graph Coloring David Eppstein Univ. of California, Irvine Dept. of Information and Computer Science The Exact Graph Coloring Problem: Given an undirected

### Testing Hereditary Properties of Non-Expanding Bounded-Degree Graphs

Testing Hereditary Properties of Non-Expanding Bounded-Degree Graphs Artur Czumaj Asaf Shapira Christian Sohler Abstract We study graph properties which are testable for bounded degree graphs in time independent

### Euclidean Geometry. We start with the idea of an axiomatic system. An axiomatic system has four parts:

Euclidean Geometry Students are often so challenged by the details of Euclidean geometry that they miss the rich structure of the subject. We give an overview of a piece of this structure below. We start

### Page 1. CSCE 310J Data Structures & Algorithms. CSCE 310J Data Structures & Algorithms. P, NP, and NP-Complete. Polynomial-Time Algorithms

CSCE 310J Data Structures & Algorithms P, NP, and NP-Complete Dr. Steve Goddard goddard@cse.unl.edu CSCE 310J Data Structures & Algorithms Giving credit where credit is due:» Most of the lecture notes

### x 2 f 2 e 1 e 4 e 3 e 2 q f 4 x 4 f 3 x 3

Karl-Franzens-Universitat Graz & Technische Universitat Graz SPEZIALFORSCHUNGSBEREICH F 003 OPTIMIERUNG und KONTROLLE Projektbereich DISKRETE OPTIMIERUNG O. Aichholzer F. Aurenhammer R. Hainz New Results

### Introduction to Logic in Computer Science: Autumn 2006

Introduction to Logic in Computer Science: Autumn 2006 Ulle Endriss Institute for Logic, Language and Computation University of Amsterdam Ulle Endriss 1 Plan for Today Now that we have a basic understanding

### 2.3 Scheduling jobs on identical parallel machines

2.3 Scheduling jobs on identical parallel machines There are jobs to be processed, and there are identical machines (running in parallel) to which each job may be assigned Each job = 1,,, must be processed

### Bicolored Shortest Paths in Graphs with Applications to Network Overlay Design

Bicolored Shortest Paths in Graphs with Applications to Network Overlay Design Hongsik Choi and Hyeong-Ah Choi Department of Electrical Engineering and Computer Science George Washington University Washington,

### Connected Identifying Codes for Sensor Network Monitoring

Connected Identifying Codes for Sensor Network Monitoring Niloofar Fazlollahi, David Starobinski and Ari Trachtenberg Dept. of Electrical and Computer Engineering Boston University, Boston, MA 02215 Email:

### On the Relationship between Classes P and NP

Journal of Computer Science 8 (7): 1036-1040, 2012 ISSN 1549-3636 2012 Science Publications On the Relationship between Classes P and NP Anatoly D. Plotnikov Department of Computer Systems and Networks,

### Resource Allocation with Time Intervals

Resource Allocation with Time Intervals Andreas Darmann Ulrich Pferschy Joachim Schauer Abstract We study a resource allocation problem where jobs have the following characteristics: Each job consumes

### GENERATING LOW-DEGREE 2-SPANNERS

SIAM J. COMPUT. c 1998 Society for Industrial and Applied Mathematics Vol. 27, No. 5, pp. 1438 1456, October 1998 013 GENERATING LOW-DEGREE 2-SPANNERS GUY KORTSARZ AND DAVID PELEG Abstract. A k-spanner

### 1 Definitions. Supplementary Material for: Digraphs. Concept graphs

Supplementary Material for: van Rooij, I., Evans, P., Müller, M., Gedge, J. & Wareham, T. (2008). Identifying Sources of Intractability in Cognitive Models: An Illustration using Analogical Structure Mapping.

### Solving Geometric Problems with the Rotating Calipers *

Solving Geometric Problems with the Rotating Calipers * Godfried Toussaint School of Computer Science McGill University Montreal, Quebec, Canada ABSTRACT Shamos [1] recently showed that the diameter of

### CMSC 858T: Randomized Algorithms Spring 2003 Handout 8: The Local Lemma

CMSC 858T: Randomized Algorithms Spring 2003 Handout 8: The Local Lemma Please Note: The references at the end are given for extra reading if you are interested in exploring these ideas further. You are

### Clique coloring B 1 -EPG graphs

Clique coloring B 1 -EPG graphs Flavia Bonomo a,c, María Pía Mazzoleni b,c, and Maya Stein d a Departamento de Computación, FCEN-UBA, Buenos Aires, Argentina. b Departamento de Matemática, FCE-UNLP, La

### princeton univ. F 13 cos 521: Advanced Algorithm Design Lecture 6: Provable Approximation via Linear Programming Lecturer: Sanjeev Arora

princeton univ. F 13 cos 521: Advanced Algorithm Design Lecture 6: Provable Approximation via Linear Programming Lecturer: Sanjeev Arora Scribe: One of the running themes in this course is the notion of

### Improved Algorithms for Data Migration

Improved Algorithms for Data Migration Samir Khuller 1, Yoo-Ah Kim, and Azarakhsh Malekian 1 Department of Computer Science, University of Maryland, College Park, MD 20742. Research supported by NSF Award

### Finding and counting given length cycles

Finding and counting given length cycles Noga Alon Raphael Yuster Uri Zwick Abstract We present an assortment of methods for finding and counting simple cycles of a given length in directed and undirected

### Analysis of Approximation Algorithms for k-set Cover using Factor-Revealing Linear Programs

Analysis of Approximation Algorithms for k-set Cover using Factor-Revealing Linear Programs Stavros Athanassopoulos, Ioannis Caragiannis, and Christos Kaklamanis Research Academic Computer Technology Institute

### SEQUENCES OF MAXIMAL DEGREE VERTICES IN GRAPHS. Nickolay Khadzhiivanov, Nedyalko Nenov

Serdica Math. J. 30 (2004), 95 102 SEQUENCES OF MAXIMAL DEGREE VERTICES IN GRAPHS Nickolay Khadzhiivanov, Nedyalko Nenov Communicated by V. Drensky Abstract. Let Γ(M) where M V (G) be the set of all vertices

### Graphs without proper subgraphs of minimum degree 3 and short cycles

Graphs without proper subgraphs of minimum degree 3 and short cycles Lothar Narins, Alexey Pokrovskiy, Tibor Szabó Department of Mathematics, Freie Universität, Berlin, Germany. August 22, 2014 Abstract

### MATHEMATICAL ENGINEERING TECHNICAL REPORTS. The Best-fit Heuristic for the Rectangular Strip Packing Problem: An Efficient Implementation

MATHEMATICAL ENGINEERING TECHNICAL REPORTS The Best-fit Heuristic for the Rectangular Strip Packing Problem: An Efficient Implementation Shinji IMAHORI, Mutsunori YAGIURA METR 2007 53 September 2007 DEPARTMENT

### Short Cycles make W-hard problems hard: FPT algorithms for W-hard Problems in Graphs with no short Cycles

Short Cycles make W-hard problems hard: FPT algorithms for W-hard Problems in Graphs with no short Cycles Venkatesh Raman and Saket Saurabh The Institute of Mathematical Sciences, Chennai 600 113. {vraman

### Nan Kong, Andrew J. Schaefer. Department of Industrial Engineering, Univeristy of Pittsburgh, PA 15261, USA

A Factor 1 2 Approximation Algorithm for Two-Stage Stochastic Matching Problems Nan Kong, Andrew J. Schaefer Department of Industrial Engineering, Univeristy of Pittsburgh, PA 15261, USA Abstract We introduce

### CSC 373: Algorithm Design and Analysis Lecture 16

CSC 373: Algorithm Design and Analysis Lecture 16 Allan Borodin February 25, 2013 Some materials are from Stephen Cook s IIT talk and Keven Wayne s slides. 1 / 17 Announcements and Outline Announcements

### Outline 2.1 Graph Isomorphism 2.2 Automorphisms and Symmetry 2.3 Subgraphs, part 1

GRAPH THEORY LECTURE STRUCTURE AND REPRESENTATION PART A Abstract. Chapter focuses on the question of when two graphs are to be regarded as the same, on symmetries, and on subgraphs.. discusses the concept

### JUST-IN-TIME SCHEDULING WITH PERIODIC TIME SLOTS. Received December May 12, 2003; revised February 5, 2004

Scientiae Mathematicae Japonicae Online, Vol. 10, (2004), 431 437 431 JUST-IN-TIME SCHEDULING WITH PERIODIC TIME SLOTS Ondřej Čepeka and Shao Chin Sung b Received December May 12, 2003; revised February

### Best Monotone Degree Bounds for Various Graph Parameters

Best Monotone Degree Bounds for Various Graph Parameters D. Bauer Department of Mathematical Sciences Stevens Institute of Technology Hoboken, NJ 07030 S. L. Hakimi Department of Electrical and Computer

### Matthias F.M. Stallmann Department of Computer Science North Carolina State University Raleigh, NC 27695-8206

Matthias F.M. Stallmann Department of Computer Science North Carolina State University Raleigh, NC 27695-8206 August, 1990 UNIFORM PLANAR EMBEDDING IS NP-COMPLETE* MATTHIAS F.M. STALLMANN Key Words. planar

### arxiv: v2 [math.co] 30 Nov 2015

PLANAR GRAPH IS ON FIRE PRZEMYSŁAW GORDINOWICZ arxiv:1311.1158v [math.co] 30 Nov 015 Abstract. Let G be any connected graph on n vertices, n. Let k be any positive integer. Suppose that a fire breaks out

### Weighted Sum Coloring in Batch Scheduling of Conflicting Jobs

Weighted Sum Coloring in Batch Scheduling of Conflicting Jobs Leah Epstein Magnús M. Halldórsson Asaf Levin Hadas Shachnai Abstract Motivated by applications in batch scheduling of jobs in manufacturing

### Offline sorting buffers on Line

Offline sorting buffers on Line Rohit Khandekar 1 and Vinayaka Pandit 2 1 University of Waterloo, ON, Canada. email: rkhandekar@gmail.com 2 IBM India Research Lab, New Delhi. email: pvinayak@in.ibm.com

### Partitioning edge-coloured complete graphs into monochromatic cycles and paths

arxiv:1205.5492v1 [math.co] 24 May 2012 Partitioning edge-coloured complete graphs into monochromatic cycles and paths Alexey Pokrovskiy Departement of Mathematics, London School of Economics and Political

### R u t c o r Research R e p o r t. Boolean functions with a simple certificate for CNF complexity. Ondřej Čepeka Petr Kučera b Petr Savický c

R u t c o r Research R e p o r t Boolean functions with a simple certificate for CNF complexity Ondřej Čepeka Petr Kučera b Petr Savický c RRR 2-2010, January 24, 2010 RUTCOR Rutgers Center for Operations

### Social Media Mining. Graph Essentials

Graph Essentials Graph Basics Measures Graph and Essentials Metrics 2 2 Nodes and Edges A network is a graph nodes, actors, or vertices (plural of vertex) Connections, edges or ties Edge Node Measures

### Survey of Terrain Guarding and Art Gallery Problems

Survey of Terrain Guarding and Art Gallery Problems Erik Krohn Abstract The terrain guarding problem and art gallery problem are two areas in computational geometry. Different versions of terrain guarding

### On the independence number of graphs with maximum degree 3

On the independence number of graphs with maximum degree 3 Iyad A. Kanj Fenghui Zhang Abstract Let G be an undirected graph with maximum degree at most 3 such that G does not contain any of the three graphs

### Offline 1-Minesweeper is NP-complete

Offline 1-Minesweeper is NP-complete James D. Fix Brandon McPhail May 24 Abstract We use Minesweeper to illustrate NP-completeness proofs, arguments that establish the hardness of solving certain problems.

### Private Approximation of Clustering and Vertex Cover

Private Approximation of Clustering and Vertex Cover Amos Beimel, Renen Hallak, and Kobbi Nissim Department of Computer Science, Ben-Gurion University of the Negev Abstract. Private approximation of search

### A 2-factor in which each cycle has long length in claw-free graphs

A -factor in which each cycle has long length in claw-free graphs Roman Čada Shuya Chiba Kiyoshi Yoshimoto 3 Department of Mathematics University of West Bohemia and Institute of Theoretical Computer Science

### Why? A central concept in Computer Science. Algorithms are ubiquitous.

Analysis of Algorithms: A Brief Introduction Why? A central concept in Computer Science. Algorithms are ubiquitous. Using the Internet (sending email, transferring files, use of search engines, online

### Discrete Mathematics & Mathematical Reasoning Chapter 10: Graphs

Discrete Mathematics & Mathematical Reasoning Chapter 10: Graphs Kousha Etessami U. of Edinburgh, UK Kousha Etessami (U. of Edinburgh, UK) Discrete Mathematics (Chapter 6) 1 / 13 Overview Graphs and Graph

### Bargaining Solutions in a Social Network

Bargaining Solutions in a Social Network Tanmoy Chakraborty and Michael Kearns Department of Computer and Information Science University of Pennsylvania Abstract. We study the concept of bargaining solutions,

### ON THE COMPLEXITY OF THE GAME OF SET. {kamalika,pbg,dratajcz,hoeteck}@cs.berkeley.edu

ON THE COMPLEXITY OF THE GAME OF SET KAMALIKA CHAUDHURI, BRIGHTEN GODFREY, DAVID RATAJCZAK, AND HOETECK WEE {kamalika,pbg,dratajcz,hoeteck}@cs.berkeley.edu ABSTRACT. Set R is a card game played with a

### 1. Nondeterministically guess a solution (called a certificate) 2. Check whether the solution solves the problem (called verification)

Some N P problems Computer scientists have studied many N P problems, that is, problems that can be solved nondeterministically in polynomial time. Traditionally complexity question are studied as languages:

### Co-Clustering under the Maximum Norm

algorithms Article Co-Clustering under the Maximum Norm Laurent Bulteau 1, Vincent Froese 2, *, Sepp Hartung 2 and Rolf Niedermeier 2 1 IGM-LabInfo, CNRS UMR 8049, Université Paris-Est Marne-la-Vallée,

### Boulder Dash is NP hard

Boulder Dash is NP hard Marzio De Biasi marziodebiasi [at] gmail [dot] com December 2011 Version 0.01:... now the difficult part: is it NP? Abstract Boulder Dash is a videogame created by Peter Liepa and

### ARTICLE IN PRESS. European Journal of Operational Research xxx (2004) xxx xxx. Discrete Optimization. Nan Kong, Andrew J.

A factor 1 European Journal of Operational Research xxx (00) xxx xxx Discrete Optimization approximation algorithm for two-stage stochastic matching problems Nan Kong, Andrew J. Schaefer * Department of

### Lecture 22: November 10

CS271 Randomness & Computation Fall 2011 Lecture 22: November 10 Lecturer: Alistair Sinclair Based on scribe notes by Rafael Frongillo Disclaimer: These notes have not been subjected to the usual scrutiny

### The Conference Call Search Problem in Wireless Networks

The Conference Call Search Problem in Wireless Networks Leah Epstein 1, and Asaf Levin 2 1 Department of Mathematics, University of Haifa, 31905 Haifa, Israel. lea@math.haifa.ac.il 2 Department of Statistics,

### The Firefighter Problem: A Structural Analysis

Electronic Colloquium on Computational Complexity, Report No. 162 (2013) The Firefighter Problem: A Structural Analysis Janka Chlebíková a, Morgan Chopin b,1 a University of Portsmouth, School of Computing,

### OHJ-2306 Introduction to Theoretical Computer Science, Fall 2012 8.11.2012

276 The P vs. NP problem is a major unsolved problem in computer science It is one of the seven Millennium Prize Problems selected by the Clay Mathematics Institute to carry a \$ 1,000,000 prize for the

### Single machine parallel batch scheduling with unbounded capacity

Workshop on Combinatorics and Graph Theory 21th, April, 2006 Nankai University Single machine parallel batch scheduling with unbounded capacity Yuan Jinjiang Department of mathematics, Zhengzhou University

### Complexity Theory. IE 661: Scheduling Theory Fall 2003 Satyaki Ghosh Dastidar

Complexity Theory IE 661: Scheduling Theory Fall 2003 Satyaki Ghosh Dastidar Outline Goals Computation of Problems Concepts and Definitions Complexity Classes and Problems Polynomial Time Reductions Examples

### Triangle deletion. Ernie Croot. February 3, 2010

Triangle deletion Ernie Croot February 3, 2010 1 Introduction The purpose of this note is to give an intuitive outline of the triangle deletion theorem of Ruzsa and Szemerédi, which says that if G = (V,

### Mathematics for Algorithm and System Analysis

Mathematics for Algorithm and System Analysis for students of computer and computational science Edward A. Bender S. Gill Williamson c Edward A. Bender & S. Gill Williamson 2005. All rights reserved. Preface

### An Optimization Approach for Cooperative Communication in Ad Hoc Networks

An Optimization Approach for Cooperative Communication in Ad Hoc Networks Carlos A.S. Oliveira and Panos M. Pardalos University of Florida Abstract. Mobile ad hoc networks (MANETs) are a useful organizational

### The Goldberg Rao Algorithm for the Maximum Flow Problem

The Goldberg Rao Algorithm for the Maximum Flow Problem COS 528 class notes October 18, 2006 Scribe: Dávid Papp Main idea: use of the blocking flow paradigm to achieve essentially O(min{m 2/3, n 1/2 }

### An Empirical Study of Two MIS Algorithms

An Empirical Study of Two MIS Algorithms Email: Tushar Bisht and Kishore Kothapalli International Institute of Information Technology, Hyderabad Hyderabad, Andhra Pradesh, India 32. tushar.bisht@research.iiit.ac.in,

### Network File Storage with Graceful Performance Degradation

Network File Storage with Graceful Performance Degradation ANXIAO (ANDREW) JIANG California Institute of Technology and JEHOSHUA BRUCK California Institute of Technology A file storage scheme is proposed

### A simpler and better derandomization of an approximation algorithm for Single Source Rent-or-Buy

A simpler and better derandomization of an approximation algorithm for Single Source Rent-or-Buy David P. Williamson Anke van Zuylen School of Operations Research and Industrial Engineering, Cornell University,

### Computational Geometry. Lecture 1: Introduction and Convex Hulls

Lecture 1: Introduction and convex hulls 1 Geometry: points, lines,... Plane (two-dimensional), R 2 Space (three-dimensional), R 3 Space (higher-dimensional), R d A point in the plane, 3-dimensional space,

### Handout #Ch7 San Skulrattanakulchai Gustavus Adolphus College Dec 6, 2010. Chapter 7: Digraphs

MCS-236: Graph Theory Handout #Ch7 San Skulrattanakulchai Gustavus Adolphus College Dec 6, 2010 Chapter 7: Digraphs Strong Digraphs Definitions. A digraph is an ordered pair (V, E), where V is the set

### arxiv:1203.1525v1 [math.co] 7 Mar 2012

Constructing subset partition graphs with strong adjacency and end-point count properties Nicolai Hähnle haehnle@math.tu-berlin.de arxiv:1203.1525v1 [math.co] 7 Mar 2012 March 8, 2012 Abstract Kim defined

### Fairness in Routing and Load Balancing

Fairness in Routing and Load Balancing Jon Kleinberg Yuval Rabani Éva Tardos Abstract We consider the issue of network routing subject to explicit fairness conditions. The optimization of fairness criteria

### Energy Efficient Monitoring in Sensor Networks

Energy Efficient Monitoring in Sensor Networks Amol Deshpande, Samir Khuller, Azarakhsh Malekian, Mohammed Toossi Computer Science Department, University of Maryland, A.V. Williams Building, College Park,

### Tools for parsimonious edge-colouring of graphs with maximum degree three. J.L. Fouquet and J.M. Vanherpe. Rapport n o RR-2010-10

Tools for parsimonious edge-colouring of graphs with maximum degree three J.L. Fouquet and J.M. Vanherpe LIFO, Université d Orléans Rapport n o RR-2010-10 Tools for parsimonious edge-colouring of graphs

### Mean Ramsey-Turán numbers

Mean Ramsey-Turán numbers Raphael Yuster Department of Mathematics University of Haifa at Oranim Tivon 36006, Israel Abstract A ρ-mean coloring of a graph is a coloring of the edges such that the average

### Degree-3 Treewidth Sparsifiers

Degree-3 Treewidth Sparsifiers Chandra Chekuri Julia Chuzhoy Abstract We study treewidth sparsifiers. Informally, given a graph G of treewidth k, a treewidth sparsifier H is a minor of G, whose treewidth

### Asymptotically Faster Algorithms for the Parameterized face cover Problem

Asymptotically Faster Algorithms for the Parameterized face cover Problem Faisal N. Abu-Khzam 1, Henning Fernau 2 and Michael A. Langston 3 1 Division of Computer Science and Mathematics, Lebanese American

### Approximation Algorithms

Approximation Algorithms or: How I Learned to Stop Worrying and Deal with NP-Completeness Ong Jit Sheng, Jonathan (A0073924B) March, 2012 Overview Key Results (I) General techniques: Greedy algorithms

### arxiv:1507.04820v1 [cs.cc] 17 Jul 2015

The Complexity of Switching and FACTS Maximum-Potential-Flow Problems Karsten Lehmann 2,1, Alban Grastien 2,1, and Pascal Van Hentenryck 1,2 arxiv:1507.04820v1 [cs.cc] 17 Jul 2015 1 Artificial Intelligence

### High degree graphs contain large-star factors

High degree graphs contain large-star factors Dedicated to László Lovász, for his 60th birthday Noga Alon Nicholas Wormald Abstract We show that any finite simple graph with minimum degree d contains a

### Tutorial 8. NP-Complete Problems

Tutorial 8 NP-Complete Problems Decision Problem Statement of a decision problem Part 1: instance description defining the input Part 2: question stating the actual yesor-no question A decision problem

### 136 CHAPTER 4. INDUCTION, GRAPHS AND TREES

136 TER 4. INDUCTION, GRHS ND TREES 4.3 Graphs In this chapter we introduce a fundamental structural idea of discrete mathematics, that of a graph. Many situations in the applications of discrete mathematics

### Existence of pure Nash equilibria (NE): Complexity of computing pure NE: Approximating the social optimum: Empirical results:

Existence Theorems and Approximation Algorithms for Generalized Network Security Games V.S. Anil Kumar, Rajmohan Rajaraman, Zhifeng Sun, Ravi Sundaram, College of Computer & Information Science, Northeastern