An ant colony algorithm for the pos/neg weighted p-median problem

Let a graph G = (V, E) with vertex set V and edge set E be given. The classical graph version of the p-median problem asks for a subset of cardinality p, so that the (weighted) sum of the minimum distances from X to all other vertices in V is minimized. We consider the semi-obnoxious case, where every vertex has either a positive or a negative weight. This gives rise to two different objective functions, namely the weighted sum of the minimum distances from X to the vertices in V\X and, differently, the sum over the minimum weighted distances from X to V\X. In this paper an Ant Colony algorithm with a tabu restriction is designed for both problems. Computational results show its superiority with respect to a previously investigated variable neighborhood search and a tabu search heuristic.This research has partially been supported by the Spezialforschungsbereich F 003 “Optimierung und Kontrolle”, Projektbereich Diskrete Optimierung.     

Efficient algorithms for a constrained k-tree core problem in a tree network

Let T=(V,E) be a free tree in which each vertex has a weight and each edge has a length. Let n=|V|. Given T and parameters k and l, a (k,l)-tree core is a subtree X of T with diameter l, having k leaves, which minimizes the sum of the weighted distances from all vertices in T to X. In this paper, two efficient algorithms are presented for finding a (k,l)-tree core of T. The first algorithm has O(n²) time complexity for the case that each edge has an arbitrary length. The second algorithm has O(lkn) time complexity for the case that the lengths of all edges are 1. The (k,l)-tree core problem has an application in distributed database systems.     

The Conditional Covering Problem on Unweighted Interval Graphs with Nonuniform Coverage Radius

Let G = (V, E) be an interval graph with n vertices and m edges. A positive integer R(x) is associated with every vertex x ? V{x\in V}. In the conditional covering problem, a vertex x ? V{x \in V} covers a vertex y ? V{y \in V} (x ≠ y) if d(x, y) ≤ R(x) where d(x, y) is the shortest distance between the vertices x and y. The conditional covering problem (CCP) finds a minimum cardinality vertex set C í V{C\subseteq V} so as to cover all the vertices of the graph and every vertex in C is also covered by another vertex of C. This problem is NP-complete for general graphs. In this paper, we propose an efficient algorithm to solve the CCP with nonuniform coverage radius in O(n ²) time, when G is an interval graph containing n vertices.     

Covering minimum spanning trees of random subgraphs

We consider the problem of finding a sparse set of edges containing the minimum spanning tree (MST) of a random subgraph of G with high probability. The two random models that we consider are subgraphs induced by a random subset of vertices, each vertex included independently with probability p, and subgraphs generated as a random subset of edges, each edge with probability p. Let n denote the number of vertices, choose p ∈ (0, 1) possibly depending on n, and let b = 1/(1 ? p). We show that in both random models, for any weighted graph G, there is a set of edges Q of cardinality O(n log_bn) that contains the minimum spanning tree of a random subgraph of G with high probability. This result is asymptotically optimal. As a consequence, we also give a bound of O(kn) on the size of the union of all minimum spanning trees of G with some k vertices (or edges) removed. More generally, we show a bound of O(n log_bn) on the size of a covering set in a matroid of rank n, which contains the minimum‐weight basis of a random subset with high probability. Also, we give a randomized algorithm that calls an MST subroutine only a polylogarithmic number of times and finds the covering set with high probability. © 2005 Wiley Periodicals, Inc. Random Struct. Alg., 2006     

Augmenting Edge-Connectivity over the Entire Range inÕ(nm) Time

For a given undirected graphG = (V, E, c_G) with edges weighted by nonnegative realsc_G:E → R ⁺ , let Λ_G(k) stand for the minimum amount of weights which needs to be added to makeG k-edge-connected, and letG*(k) be the resulting graph obtained fromG. This paper first shows that function Λ_Gover the entire rangek [0, +∞] can be computed inO(nm + n² log n) time, and then shows that allG*(k) in the entire range can be obtained fromO(n log n) weighted cycles, and such cycles can be computed inO(nm + n² log n) time, wherenandmare the numbers of vertices and edges, respectively.     

Sum List Coloring Graphs

Let G=(V,E) be a graph with n vertices and e edges. The sum choice number of G is the smallest integer p such that there exist list sizes (f(v):v ∈ V) whose sum is p for which G has a proper coloring no matter which color lists of size f(v) are assigned to the vertices v. The sum choice number is bounded above by n+e. If the sum choice number of G equals n+e, then G is sum choice greedy. Complete graphs K_n are sum choice greedy as are trees. Based on a simple, but powerful, lemma we show that a graph each of whose blocks is sum choice greedy is also sum choice greedy. We also determine the sum choice number of K_2,n, and we show that every tree on n vertices can be obtained from K_n by consecutively deleting single edges where all intermediate graphs are sc-greedy.     

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.     

Efficient and Practical Algorithms for Sequential Modular Decomposition

A module of an undirected graph G = (V, E) is a set X of vertices that have the same set of neighbors in V\X. The modular decomposition is a unique decomposition of the vertices into nested modules. We give a practical algorithm with an O(n + mα(m, n)) time bound and a variant with a linear time bound.     

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.     

A parallel search algorithm for directed acyclic graphs

A parallel algorithm for depth-first searching of a directed acyclic graph (DAG) on a shared memory model of a SIMD computer is proposed. The algorithm uses two parallel tree traversal algorithms, one for the preorder traversal and the other for therpostorder traversal of an ordered tree. Each of these traversal algorithms has a time complexity ofO(logn) whenO(n) processors are used,n being the number of vertices in the tree. The parallel depth-first search algorithm for a directed acyclic graphG withn vertices has a time complexity ofO((logn)²) whenO(n ^2.81/logn) processors are used.     

Distributing vertices on Hamiltonian cycles

Let G be a graph of order n and 3≤t≤n/4 be an integer. Recently, Kaneko and Yoshimoto [J Combin Theory Ser B 81(1) (2001), 100–109] provided a sharp δ(G) condition such that for any set X of t vertices, G contains a hamiltonian cycle H so that the distance along H between any two vertices of X is at least n/2t. In this article, minimum degree and connectivity conditions are determined such that for any graph G of sufficiently large order n and for any set of t vertices X?V(G), there is a hamiltonian cycle H so that the distance along H between any two consecutive vertices of X is approximately n/t. Furthermore, the minimum degree threshold is determined for the existence of a hamiltonian cycle H such that the vertices of X appear in a prescribed order at approximately predetermined distances along H. © 2011 Wiley Periodicals, Inc. J Graph Theory 69: 28–45, 2012     

Degree Bounded Spanning Trees

In this paper, we give a sufficient condition for a graph to have a degree bounded spanning tree. Let n ≥ 1, k ≥ 3, c ≥ 0 and G be an n-connected graph. Suppose that for every independent set ${S \subseteq V(G)}In this paper, we give a sufficient condition for a graph to have a degree bounded spanning tree. Let n ≥ 1, k ≥ 3, c ≥ 0 and G be an n-connected graph. Suppose that for every independent set S í V(G){S \subseteq V(G)} of cardinality n(k−1) + c + 2, there exists a vertex set X í S{X \subseteq S} of cardinality k such that the degree sum of vertices in X is at least |V(G)| − c −1. Then G has a spanning tree T with maximum degree at most k+éc/nù{k+\lceil c/n\rceil} and ?_{v ? V(T)}max{d_T(v)-k,0} ￡ c{\sum_{v\in V(T)}\max\{d_T(v)-k,0\}\leq c} .     

The Sigma Chromatic Number of a Graph

For a nontrivial connected graph G, let ${c: V(G)\to {{\mathbb N}}}For a nontrivial connected graph G, let c: V(G)? \mathbb N{c: V(G)\to {{\mathbb N}}} be a vertex coloring of G, where adjacent vertices may be colored the same. For a vertex v of G, let N(v) denote the set of vertices adjacent to v. The color sum σ(v) of v is the sum of the colors of the vertices in N(v). If σ(u) ≠ σ(v) for every two adjacent vertices u and v of G, then c is called a sigma coloring of G. The minimum number of colors required in a sigma coloring of a graph G is called its sigma chromatic number σ(G). The sigma chromatic number of a graph G never exceeds its chromatic number χ(G) and for every pair a, b of positive integers with a ≤ b, there exists a connected graph G with σ(G) = a and χ(G) = b. There is a connected graph G of order n with σ(G) = k for every pair k, n of positive integers with k ≤n if and only if k ≠ n − 1. Several other results concerning sigma chromatic numbers are presented.     

A polynomial time approximation scheme for the two-source minimum routing cost spanning trees

Let G be an undirected graph with nonnegative edge lengths. Given two vertices as sources and all vertices as destinations, we investigated the problem how to construct a spanning tree of G such that the sum of distances from sources to destinations is minimum. In the paper, we show the NP-hardness of the problem and present a polynomial time approximation scheme. For any >0, the approximation scheme finds a (1+)-approximation solution in O(n^1/+1) time. We also generalize the approximation algorithm to the weighted case for distances that form a metric space.     

All-Pairs Small-Stretch Paths

Let G = (V, E) be a weighted undirected graph. A path between u, v  V is said to be of stretch t if its length is at most t times the distance between u and v in the graph. We consider the problem of finding small-stretch paths between all pairs of vertices in the graph G.It is easy to see that finding paths of stretch less than 2 between all pairs of vertices in an undirected graph with n vertices is at least as hard as the Boolean multiplication of two n × n matrices. We describe three algorithms for finding small-stretch paths between all pairs of vertices in a weighted graph with n vertices and m edges. The first algorithm, STRETCH₂, runs in Õ(n^3/2m^1/2) time and finds stretch 2 paths. The second algorithm, STRETCH_7/3, runs in Õ(n^7/3) time and finds stretch 7/3 paths. Finally, the third algorithm, STRETCH₃, runs in Õ(n²) and finds stretch 3 paths.Our algorithms are simpler, more efficient and more accurate than the previously best algorithms for finding small-stretch paths. Unlike all previous algorithms, our algorithms are not based on the construction of sparse spanners or sparse neighborhood covers.     

Random Subgraphs of Cayley Graphs overp-Groups

The subject of this paper is the size of the largest component in random subgraphs of Cayley graphs, X_n, taken over a class of p -groups, G_n. G_nconsists of p -groups, G_n, with the following properties: (i)G_n / Φ(G_n)  = F_pⁿ, where Φ(G_n) is the Frattini subgroup and (ii) | G_n|  ≤ n^Kn, where K is some positive constant. We consider Cayley graphs X_n = Γ(G_n, S_n′), where S_n′ = S_n  S_n ^− 1, and S_nis a minimal G_n-generating set. By selecting G_n-elements with the independent probability λ_nwe induce random subgraphs of X_n. Our main result is, that there exists a positive constant c >  0 such that for λ_n = c ln(| S_n′ |) / | S_n′ | the largest component of random induced subgraphs of X_ncontains almost all vertices.     

Efficient Algorithms for Finding a Core of a Tree with a Specified Length

Acoreof a graphGis a pathPinGthat is central with respect to the property of minimizingd(P)=∑_v∈V(G)d(v, P), whered(v, P) is the distance from vertexvto pathP. This paper presents efficient algorithms for finding a core of a tree with a specified length. The sequential algorithm runs inO(n log n) time, wherenis the size of the tree. The parallel algorithm runs inO(log²n) time usingO(n) processors on an EREW PRAM model.     

Some results on generalized exponents

A digraph G = (V, E) is primitive if, for some positive integer k, there is a u → v walk of length k for every pair u, v of vertices of V. The minimum such k is called the exponent of G, denoted exp(G). The exponent of a vertex u ∈ V, denoted exp(u), is the least integer k such that there is a u → v walk of length k for each v ∈ V. For a set X ⊆ V, exp(X) is the least integer k such that for each v ∈ V there is a X → v walk of length k, i.e., a u → v walk of length k for some u ∈ X. Let F(G, k) : = max{exp(X) : |X| = k} and F(n, k) : = max{F(G, k) : |V| = n}, where |X| and |V| denote the number of vertices in X and V, respectively. Recently, B. Liu and Q. Li proved F(n, k) = (n − k)(n − 1) + 1 for all 1 ≤ k ≤ n − 1. In this article, for each k, 1 ≤ k ≤ n − 1, we characterize the digraphs G such that F(G, k) = F(n, k), thereby answering a question of R. Brualdi and B. Liu. We also find some new upper bounds on the (ordinary) exponent of G in terms of the maximum outdegree of G, Δ⁺(G) = max{d⁺(u) : u ∈ V}, and thus obtain a new refinement of the Wielandt bound (n − 1)² + 1. © 1998 John Wiley & Sons, Inc. J. Graph Theory 28: 215–225, 1998     

Neighborhoods and Covering Vertices by Cycles

G =(V,E) is a 2-connected graph, and X is a set of vertices of G such that for every pair x,x' in X, , and the minimum degree of the induced graph <X> is at least 3, then X is covered by one cycle. This result will be in fact generalised by considering tuples instead of pairs of vertices. Let be the minimum degree in the induced graph <X>. For any , . If , and , then X is covered by at most (p-1) cycles of G. If furthermore , (p-1) cycles are sufficient. So we deduce the following: Let p and t () be two integers. Let G be a 2-connected graph of order n, of minimum degree at least t. If , and , then V is covered by at most cycles, where k is the connectivity of G. If furthermore , (p-1) cycles are sufficient. In particular, if and , then G is hamiltonian. Received April 3, 1998     

Incremental algorithms for minimal length paths

We consider the problem of maintaining on-line a solution to the All Pairs Shortest Paths Problem in a directed graph G = (V,E) where edges may be dynamically inserted or have their cost decreased. For the case of integer edge costs in a given range [1…C], we introduce a new data structure which is able to answer queries concerning the length of the shortest path between any two vertices in constant time and to trace out the shortest path between any two vertices in time linear in the number of edges reported. The total time required to maintain the data structure under a sequence of at most O(n²) edge insertions and at most O(Cn²) edge cost decreases is O(Cn³ log(nC)) in the worst case, where n is the total number of vertices in G. For the case of unit edge costs, the total time required to maintain the data structure under a sequence of at most O(n²) insertions of edges becomes O(n³ logn) in the worst case. The same bounds can be achieved for the problem of maintaining on-line longest paths in directed acyclic graphs. All our algorithms improve previously known algorithms and are only a logarithmic factor away from the best possible bounds.