Efficiency in exponential time for domination-type problems

We design fast exponential time algorithms for some intractable graph-theoretic problems. Our main result states that a minimum optional dominating set in a graph of order n can be found in time O^∗(1.8899ⁿ). Our methods to obtain this result involve matching techniques.The list of the considered problems includes Minimum Maximal Matching, 3-Colourability, Minimum Dominating Edge Set, Minimum Connected Dominating Set (∼Maximum Leaf Spanning Tree), Minimum Independent Dominating Set and Minimum Dominating set.     

On B(5, 18) Groups

A group G is said to be a B(n, k) group if for any n-element subset A of G, |A²| ≤k. In this paper, a characterization of B(5, 18) groups is given. It is shown that G is a B(5, 18) group if and only if one of the following statements holds: (1) G is abelian; (2) |G| ≤18; (3) G ? ? a, b | a⁵ = b⁴ = 1, a^b = a^?1 ?.     

Covering times of random walks on bounded degree trees and other graphs

The motivating problem for this paper is to find the expected covering time of a random walk on a balanced binary tree withn vertices. Previous upper bounds for general graphs ofO(|V| |E|)⁽¹⁾ andO(|V| |E|/d _min)⁽²⁾ imply an upper bound ofO(n ²). We show an upper bound on general graphs ofO( |E| log |V|), which implies an upper bound ofO(n log² n). The previous lower bound was (|V| log |V|) for trees.⁽²⁾ In our main result, we show a lower bound of (|V| (log _{d max} |V|)²) for trees, which yields a lower bound of (n log² n). We also extend our techniques to show an upper bound for general graphs ofO(max{E T_i} log |V|).     

Steiner centers and Steiner medians of graphs

Let G be a connected graph and S a set of vertices of G. The Steiner distance of S is the smallest number of edges in a connected subgraph of G that contains S and is denoted by d_G(S) or d(S). The Steiner n-eccentricity e_n(v) and Steiner n-distance d_n(v) of a vertex v in G are defined as e_n(v)=max{d(S)| SV(G), |S|=n and vS} and d_n(v)=∑{d(S)| SV(G), |S|=n and vS}, respectively. The Steiner n-center C_n(G) of G is the subgraph induced by the vertices of minimum n-eccentricity. The Steiner n-median M_n(G) of G is the subgraph induced by those vertices with minimum Steiner n-distance. Let T be a tree. Oellermann and Tian [O.R. Oellermann, S. Tian, Steiner centers in graphs, J. Graph Theory 14 (1990) 585–597] showed that C_n(T) is contained in C_n+1(T) for all n2. Beineke et al. [L.W. Beineke, O.R. Oellermann, R.E. Pippert, On the Steiner median of a tree, Discrete Appl. Math. 68 (1996) 249–258] showed that M_n(T) is contained in M_n+1(T) for all n2. Then, Oellermann [O.R. Oellermann, On Steiner centers and Steiner medians of graphs, Networks 34 (1999) 258–263] asked whether these containment relationships hold for general graphs. In this note we show that for every n2 there is an infinite family of block graphs G for which C_n(G)C_n+1(G). We also show that for each n2 there is a distance–hereditary graph G such that M_n(G)M_n+1(G). Despite these negative examples, we prove that if G is a block graph then M_n(G) is contained in M_n+1(G) for all n2. Further, a linear time algorithm for finding the Steiner n-median of a block graph is presented and an efficient algorithm for finding the Steiner n-distances of all vertices in a block graph is described.     

On packing 3‐vertex paths in a graph

Let G be a connected graph and let eb(G) and λ(G) denote the number of end‐blocks and the maximum number of disjoint 3‐vertex paths Λ in G. We prove the following theorems on claw‐free graphs: (t1) if G is claw‐free and eb(G) ≤ 2 (and in particular, G is 2‐connected) then λ(G) = ⌊| V(G)|/3⌋; (t2) if G is claw‐free and eb(G) ≥ 2 then λ(G) ≥ ⌊(| V(G) | − eb(G) + 2)/3 ⌋; and (t3) if G is claw‐free and Δ^*‐free then λ(G) = ⌊| V(G) |/3⌋ (here Δ^* is a graph obtained from a triangle Δ by attaching to each vertex a new dangling edge). We also give the following sufficient condition for a graph to have a Λ‐factor: Let n and p be integers, 1 ≤ p ≤ n − 2, G a 2‐connected graph, and |V(G)| = 3n. Suppose that G − S has a Λ‐factor for every S ⊆ V(G) such that |S| = 3p and both V(G) − S and S induce connected subgraphs in G. Then G has a Λ‐factor. © 2001 John Wiley & Sons, Inc. J Graph Theory 36: 175–197, 2001     

Geodetic and Steiner geodetic sets in 3-Steiner distance hereditary graphs

Let G be a connected graph and S⊆V(G). Then the Steiner distance of S, denoted by d_G(S), is the smallest number of edges in a connected subgraph of G containing S. Such a subgraph is necessarily a tree called a Steiner tree for S. The Steiner interval for a set S of vertices in a graph, denoted by I(S) is the union of all vertices that belong to some Steiner tree for S. If S={u,v}, then I(S) is the interval I[u,v] between u and v. A connected graph G is 3-Steiner distance hereditary (3-SDH) if, for every connected induced subgraph H of order at least 3 and every set S of three vertices of H, d_H(S)=d_G(S). The eccentricity of a vertex v in a connected graph G is defined as e(v)=max{d(v,x)|x∈V(G)}. A vertex v in a graph G is a contour vertex if for every vertex u adjacent with v, e(u)?e(v). The closure of a set S of vertices, denoted by I[S], is defined to be the union of intervals between pairs of vertices of S taken over all pairs of vertices in S. A set of vertices of a graph G is a geodetic set if its closure is the vertex set of G. The smallest cardinality of a geodetic set of G is called the geodetic number of G and is denoted by g(G). A set S of vertices of a connected graph G is a Steiner geodetic set for G if I(S)=V(G). The smallest cardinality of a Steiner geodetic set of G is called the Steiner geodetic number of G and is denoted by sg(G). We show that the contour vertices of 3-SDH and HHD-free graphs are geodetic sets. For 3-SDH graphs we also show that g(G)?sg(G). An efficient algorithm for finding Steiner intervals in 3-SDH graphs is developed.     

A Parallel Algorithm for Computing Minimum Spanning Trees

present a simple and implementable algorithm that computes a minimum spanning tree of an undirected weighted graph G = (V, E) of n = |V| vertices and m = |E| edges on an EREW PRAM in O(log^3/2n) time using n + m processors. This represents a substantial improvement in the running time over the previous results for this problem using at the same time the weakest of the PRAM models. It also implies the existence of algorithms having the same complexity bounds for the EREW PRAM, for connectivity, ear decomposition, biconnectivity, strong orientation, st-numbering and Euler tours problems.     

New Algorithm for Ordered Tree-to-Tree Correction Problem

The ordered tree-to-tree correction problem is to compute the minimum edit cost of transforming one ordered tree to another one. This paper presents a new algorithm for this problem. Given two ordered trees S and T, our algorithm runs in O(|S| |T| + min { ²_S|T| + ^2.5_{S T}, ²_T|S| + ^2.5_{T S}) time, where _S denotes the number of leaves of S and _S denotes the depth of S. The previous best algorithms for this problem run in O(|S| |T| min { _S, _S} min { _T, _T}) time (K. Zhang and D. Shasha, SIAM J. Comput.18, No. 6 (1989), 1245–1262) and in O(min {|S|²|T| log₂ |T|, |T|²|S| log₂ |S|}) time (P. N. Klein, in “Algorithms—ESA'98, 6th Annual European Symposium” (G. Bilardi, G. F. Italiano, A. Pietracaprina, and G. Pucci, Eds.), Lecture Notes in Computer Science, Vol. 1461, pp. 91–102, Springer-Verlag, Berlin/New York, 1998). As a comparison, our algorithm is asymptotically faster for certain kind of trees.     

From steiner centers to steiner medians

The Steiner distance of set S of vertices in a connected graph G is the minimum number of edges in a connected subgraph of G containing S. For n ≥ 2, the Steiner n-eccentricity e_n(v) of a vertex v of a graph G is the maximum Steiner distance among all sets S of n vertices of G that contain v. The Steiner n-center of G is the subgraph induced by those vertices of G having minimum n-eccentricity. The Steiner n-distance of a vertex v of G is defined as . The Steiner n-median of G is the subgraph of G induced by the vertices of G of minimum Steiner n-distance. Known algorithms for finding the Steiner n-centers and Steiner n-medians of trees are used to show that the distance between the Steiner n-centre and Steiner n-median of a tree can be arbitrarily large. Centrality measures that allow every vertex on a shortest path from the Steiner n-center to the Steiner n-median of a tree to belong to the “center” with respect to one of these measures are introduced and several proeprties of these centrality measures are established. © 1995 John Wiley & Sons, Inc.     

Large centralizers in finite solvable groups

In 1955 R. Brauer and K. A. Fowler showed that ifG is a group of even order >2, and the order |Z(G)| of the center ofG is odd, then there exists a strongly real) elementx∈G−Z whose centralizer satisfies|C _G(x)|>|G|^1/3. In Theorem 1 we show that every non-abeliansolvable groupG contains an elementx∈G−Z such that|C _G(x)|>[G:G′∩Z]^1/2 (and thus|C _G(x)|>|G|^1/3). We also note that if non-abelianG is either metabelian, nilpotent or (more generally) supersolvable, or anA-group, or any Frobenius group, then|C _G(x)|>|G|^1/2 for somex∈G−Z. In Theorem 2 we prove that every non-abelian groupG of orderp ^mqⁿ (p, q primes) contains a proper centralizer of order >|G|^1/2. Finally, in Theorem 3 we show that theaverage |C(x)|, x∈G, is ≧c|G| ^1/3 for metabelian groups, wherec is constant and the exponent 1/3 is best possible.     

Exact algorithms for dominating set

The measure and conquer approach has proven to be a powerful tool to analyse exact algorithms for combinatorial problems like Dominating Set and Independent Set. This approach is used in this paper to obtain a faster exact algorithm for Dominating Set. We obtain this algorithm by considering a series of branch and reduce algorithms. This series is the result of an iterative process in which a mathematical analysis of an algorithm in the series with measure and conquer results in a convex or quasiconvex programming problem. The solution, by means of a computer, to this problem not only gives a bound on the running time of the algorithm, but can also give an indication on where to look for a new reduction rule, often giving a new, possibly faster algorithm. As a result, we obtain an O(1.4969ⁿ) time and polynomial space algorithm.     

Graph topologies induced by edge lengths

Let G be a graph each edge e of which is given a length ?(e). This naturally induces a distance d_?(x,y) between any two vertices x,y, and we let |G|_? denote the completion of the corresponding metric space. It turns out that several well-studied topologies on infinite graphs are special cases of |G|_?. Moreover, it seems that |G|_? is the right setting for studying various problems. The aim of this paper is to introduce |G|_?, providing basic facts, motivating examples and open problems, and indicate possible applications.Parts of this work suggest interactions between graph theory and other fields, including algebraic topology and geometric group theory.     

NP-completeness for minimizing maximum edge length in grid embeddings

Given an embedding f: G →$\text{Z}$² of a graph G in the two-dimensional lattice, let |f| be the maximum L₁ distance between points f(x) and f(y) where xy is an edge of G. Let B₂(G) be the minimum |f| over all embeddings f. It is shown that the determination of B₂(G) for arbitrary G is NP-complete. Essentially the same proof can be used in showing the NP-completeness of minimizing |f| over all embeddings f: G → $\text{Z}$ⁿ of G into the n-dimensional integer lattice for any fixed n ≥ 2.     

An efficient fixed-parameter algorithm for 3-Hitting Set

Given a collection C of subsets of size three of a finite set S and a positive integer k, the 3-Hitting Set problem is to determine a subset S′S with |S′|k, so that S′ contains at least one element from each subset in C. The problem is NP-complete, and is motivated, for example, by applications in computational biology. Improving previous work, we give an O(2.270^k+n) time algorithm for 3-Hitting Set, which is efficient for small values of k, a typical occurrence in some applications. For d-Hitting Set we present an O(c^k+n) time algorithm with c=d−1+O(d⁻¹).     

On Cycles in 3-Connected Graphs

 Let G be a 3-connected graph of order n and S a subset of vertices. Denote by δ(S) the minimum degree (in G) of vertices of S. Then we prove that the circumference of G is at least min{|S|, 2δ(S)} if the degree sum of any four independent vertices of S is at least n+6. A cycle C is called S-maximum if there is no cycle C ^′ with |C ^′∩S|>|C∩S|. We also show that if ∑⁴ _i=1 d(a _i)≥n+3+|⋂⁴ _i=1 N(a _i)| for any four independent vertices a ₁, a ₂, a ₃, a ₄ in S, then G has an S-weak-dominating S-maximum cycle C, i.e. an S-maximum cycle such that every component in G−C contains at most one vertex in S. Received: March 9, 1998 Revised: January 7, 1999     

Dynamic Text Indexing under String Updates

This paper generalizes the dynamic text indexing problem, introduced in [15], to insertion and deletion of strings. The problem is to quickly answer on-line queries about the occurrences of arbitrary pattern strings in a text that may change due to insertion or deletion of strings from it. To treat strings as atomic objects, we provide new sequential techniques and related data structures, which combine the suffix tree with the naming technique used in parallel computation on strings. We also introduce a geometric interpretation of the problem of finding the occurrences of a pattern in a given substring of the text. As a result, the algorithm allows the insertion in the text of a stringSinO(|S| log(n + |S|)) amortized time, and the deletion from the text of a stringSinO(|S| log n) amortized time, wherenis the length of the current text. A pattern search requiresO(p log p + upd ( + log p) + pocc) worst-case time, wherepis the length of the pattern andpoccis the number of its occurrences in the current text, obtained after the execution ofupdupdate operations. This solution requiresO(n² log n) space, which is not initialized.We also provide a technique to reduce the space toO(n log n), yielding a solution that requiresO((p + upd) log p + pocc) query time in the worst-case,O(|S| log^3/2(|S| + n)) amortized time to insert a stringSin, andO(|S| log^3/2n) amortized time to delete a stringSfrom the current text.Furthermore, we use our techniques to solve the novel on-line dynamic tree matching problem that requires the on-line detection of the occurrences of an arbitrary subtree in a forest of ordered labeled trees. The forest may change due to insertion or deletion of subtrees or by renaming of some nodes. Such a problem is solved by a simple reduction to the dynamic text indexing problem.     

Cycles in bipartite graphs

For k an integer, let G(a, b, k) denote a simple bipartite graph with bipartition (A, B) where |A| = a ≥ 2, |B| = b ≥ k ≥ 2, and each vertex of A has degree at least k. We prove two results concerning the existence of cycles in G(a, b, k).     

Dominating sets in triangulations on surfaces

Let G be a graph and let S?V(G). We say that S is dominating in G if each vertex of G is in S or adjacent to a vertex in S. We show that every triangulation on the torus and the Klein bottle with n vertices has a dominating set of cardinality at most $\frac{n}{3}Let G be a graph and let S?V(G). We say that S is dominating in G if each vertex of G is in S or adjacent to a vertex in S. We show that every triangulation on the torus and the Klein bottle with n vertices has a dominating set of cardinality at most $\frac{n}{3}$. Moreover, we show that the same conclusion holds for a triangulation on any non‐spherical surface with sufficiently large representativity. These results generalize that for plane triangulations proved by Matheson and Tarjan (European J Combin 17 (1996), 565–568), and solve a conjecture by Plummer (Private Communication). © 2009 Wiley Periodicals, Inc. J Graph Theory 63: 17–30, 2010     

On the toughness index of planar graphs

The toughness indexτ(G) of a graph G is defined to be the largest integer t such that for any S ? V(G) with |S| > t, c(G - S) < |S| - t, where c(G - S) denotes the number of components of G - S. In particular, 1-tough graphs are exactly those graphs for which τ(G) ≥ 0. In this paper, it is shown that if G is a planar graph, then τ(G) ≥ 2 if and only if G is 4-connected. This result suggests that there may be a polynomial-time algorithm for determining whether a planar graph is 1-tough, even though the problem for general graphs is NP-hard. The result can be restated as follows: a planar graph is 4-connected if and only if it remains 1-tough whenever two vertices are removed. Hence it establishes a weakened version of a conjecture, due to M. D. Plummer, that removing 2 vertices from a 4-connected planar graph yields a Hamiltonian graph.     

An exact algorithm for solving the vertex separator problem

Given G = (V, E) a connected undirected graph and a positive integer β(|V|), the vertex separator problem is to find a partition of V into no-empty three classes A, B, C such that there is no edge between A and B, max{|A|, |B|} ≤ β(|V|) and |C| is minimum. In this paper we consider the vertex separator problem from a polyhedral point of view. We introduce new classes of valid inequalities for the associated polyhedron. Using a natural lower bound for the optimal solution, we present successful computational experiments.