A factoring approach for the Steiner tree problem in undirected networks

In communication networks, many applications, such as video on demand and video conferencing, must establish a communications tree that spans a subset K in a vertex set. The source node can then send identical data to all nodes in set K along this tree. This kind of communication is known as multicast communication. A network optimization problem, called the Steiner tree problem (STP), is presented to find a least cost multicasting tree. In this paper, an O(|E|) algorithm is presented to find a minimum Steiner tree for series-parallel graphs where |E| is the number of edges. Based on this algorithm, we proposed an O(2^2c·|E|) algorithm to solve the Steiner tree problem for general graphs where c is the number of applied factoring procedures. The c value is strongly related to the topology of a given graph. This is quite different from other algorithms with exponential time complexities in |K|.     

A Faster Algorithm for Computing the Principal Sequence of Partitions of a Graph

We consider the following problem: given an undirected weighted graph G=(V,E,c) with nonnegative weights, minimize function c(δ(Π))−λ|Π| for all values of parameter λ. Here Π is a partition of the set of nodes, the first term is the cost of edges whose endpoints belong to different components of the partition, and |Π| is the number of components. The current best known algorithm for this problem has complexity O(|V|²) maximum flow computations. We improve it to |V| parametric maximum flow computations. We observe that the complexity can be improved further for families of graphs which admit a good separator, e.g. for planar graphs.     

Geometric Representation of Graphs in Low Dimension Using Axis Parallel Boxes

An axis-parallel k-dimensional box is a Cartesian product R ₁×R ₂×???×R _k where R _i (for 1≤i≤k) is a closed interval of the form [a _i ,b _i ] on the real line. For a graph G, its boxicity box?(G) is the minimum dimension k, such that G is representable as the intersection graph of (axis-parallel) boxes in k-dimensional space. The concept of boxicity finds applications in various areas such as ecology, operations research etc. A number of NP-hard problems are either polynomial time solvable or have much better approximation ratio on low boxicity graphs. For example, the max-clique problem is polynomial time solvable on bounded boxicity graphs and the maximum independent set problem for boxicity d graphs, given a box representation, has a $\lfloor 1+\frac{1}{c}\log n\rfloor^{d-1}An axis-parallel k-dimensional box is a Cartesian product R ₁×R ₂×⋅⋅⋅×R _k where R _i (for 1≤i≤k) is a closed interval of the form [a _i ,b _i ] on the real line. For a graph G, its boxicity box (G) is the minimum dimension k, such that G is representable as the intersection graph of (axis-parallel) boxes in k-dimensional space. The concept of boxicity finds applications in various areas such as ecology, operations research etc. A number of NP-hard problems are either polynomial time solvable or have much better approximation ratio on low boxicity graphs. For example, the max-clique problem is polynomial time solvable on bounded boxicity graphs and the maximum independent set problem for boxicity d graphs, given a box representation, has a ?1+\frac1clogn?^d-1\lfloor 1+\frac{1}{c}\log n\rfloor^{d-1} approximation ratio for any constant c≥1 when d≥2. In most cases, the first step usually is computing a low dimensional box representation of the given graph. Deciding whether the boxicity of a graph is at most 2 itself is NP-hard.     

A Sequential Algorithm for Generating Random Graphs

We present a nearly-linear time algorithm for counting and randomly generating simple graphs with a given degree sequence in a certain range. For degree sequence (d _i ) _i=1 ⁿ with maximum degree d _max?=O(m ^1/4?τ ), our algorithm generates almost uniform random graphs with that degree sequence in time O(md _max?) where $m=\frac{1}{2}\sum_{i}d_{i}We present a nearly-linear time algorithm for counting and randomly generating simple graphs with a given degree sequence in a certain range. For degree sequence (d _i ) _i=1 ⁿ with maximum degree d _max=O(m ^1/4−τ ), our algorithm generates almost uniform random graphs with that degree sequence in time O(md _max) where m=\frac12?_id_im=\frac{1}{2}\sum_{i}d_{i} is the number of edges in the graph and τ is any positive constant. The fastest known algorithm for uniform generation of these graphs (McKay and Wormald in J. Algorithms 11(1):52–67, 1990) has a running time of O(m ² d _max²). Our method also gives an independent proof of McKay’s estimate (McKay in Ars Combinatoria A 19:15–25, 1985) for the number of such graphs.     

An efficient parallel strategy for the two-fixed-endpoint Hamiltonian path problem on distance-hereditary graphs

In this paper, we solve the two-fixed-endpoint Hamiltonian path problem on distance-hereditary graphs efficiently in parallel. Let T_d(|V|,|E|) and P_d(|V|,|E|) denote the parallel time and processor complexities, respectively, required to construct a decomposition tree of a distance-hereditary graph G=(V,E) on a PRAM model M_d. We show that this problem can be solved in O(T_d(|V|,|E|)+log|V|) time using O(P_d(|V|,|E|)+(|V|+|E|)/log|V|) processors on M_d. Moreover, if G is represented by its decomposition tree form, the problem can be solved optimally in O(log|V|) time using O((|V|+|E|)/log|V|) processors on an EREW PRAM. We also obtain a linear-time algorithm which is faster than the previous known O(|V|³) sequential algorithm.     

Counting triangles in real-world networks using projections

Triangle counting is an important problem in graph mining. Two frequently used metrics in complex network analysis that require the count of triangles are the clustering coefficients and the transitivity ratio of the graph. Triangles have been used successfully in several real-world applications, such as detection of spamming activity, uncovering the hidden thematic structure of the web and link recommendation in online social networks. Furthermore, the count of triangles is a frequently used network statistic in exponential random graph models. However, counting the number of triangles in a graph is computationally expensive. In this paper, we propose the EigenTriangle and EigenTriangleLocal algorithms to estimate the number of triangles in a graph. The efficiency of our algorithms is based on the special spectral properties of real-world networks, which allow us to approximate accurately the number of triangles. We verify the efficacy of our method experimentally in almost 160 experiments using several Web Graphs, social, co-authorship, information, and Internet networks where we obtain significant speedups with respect to a straightforward triangle counting algorithm. Furthermore, we propose an algorithm based on Fast SVD which allows us to apply the core idea of the EigenTriangle algorithm on graphs which do not fit in the main memory. The main idea is a simple node-sampling process according to which node i is selected with probability \fracd_i2m{\frac{d_i}{2m}} where d _i is the degree of node i and m is the total number of edges in the graph. Our theoretical contributions also include a theorem that gives a closed formula for the number of triangles in Kronecker graphs, a model of networks which mimics several properties of real-world networks.     

A branch, bound, and remember algorithm for the 1|r i |∑t i scheduling problem

This paper presents a modified Branch and Bound (B&B) algorithm called, the Branch, Bound, and Remember (BB&R) algorithm, which uses the Distributed Best First Search (DBFS) exploration strategy for solving the 1|r _i |∑t _i scheduling problem, a single machine scheduling problem where the objective is to find a schedule with the minimum total tardiness. Memory-based dominance strategies are incorporated into the BB&R algorithm. In addition, a modified memory-based dynamic programming algorithm is also introduced to efficiently compute lower bounds for the 1|r _i |∑t _i scheduling problem. Computational results are reported, which shows that the BB&R algorithm with the DBFS exploration strategy outperforms the best known algorithms reported in the literature.     

Parallel Algorithms for Series Parallel Graphs and Graphs with Treewidth Two 1

In this paper a parallel algorithm is given that, given a graph G=(V,E) , decides whether G is a series parallel graph, and, if so, builds a decomposition tree for G of series and parallel composition rules. The algorithm uses O(log \kern -1pt |E|log ^\ast \kern -1pt |E|) time and O(|E|) operations on an EREW PRAM, and O(log \kern -1pt |E|) time and O(|E|) operations on a CRCW PRAM. The results hold for undirected as well as for directed graphs. Algorithms with the same resource bounds are described for the recognition of graphs of treewidth two, and for constructing tree decompositions of treewidth two. Hence efficient parallel algorithms can be found for a large number of graph problems on series parallel graphs and graphs with treewidth two. These include many well-known problems like all problems that can be stated in monadic second-order logic. Received July 15, 1997; revised January 29, 1999, and June 23, 1999.     

A probabilistic algorithm for vertex connectivity of graphs

A probabilistic algorithm is presented which computes the vertex connectivity of an undirected graph G = (V,E) in expected time $\text{O((-log ε|V|}{}^{\mathrm{<mtext>3</mtext><mtext>2</mtext>}}\text{|E|)}$ with error probability at most e provided that |E|<frcase|1/2d|V|² for some universal constant d<1.     

A Linear Time Algorithm for the Arc Disjoint Menger Problem in Planar Directed Graphs

Given a graph G=(V,E) and two vertices s,t ∈ V , s\neq t , the Menger problem is to find a maximum number of disjoint paths connecting s and t . Depending on whether the input graph is directed or not, and what kind of disjointness criterion is demanded, this general formulation is specialized to the directed or undirected vertex, and the edge or arc disjoint Menger problem, respectively. For planar graphs the edge disjoint Menger problem has been solved to optimality [W2], while the fastest algorithm for the arc disjoint version is Weihe's general maximum flow algorithm for planar networks [W1], which has running time \bf O (|V| log |V|) . Here we present a linear time, i.e., asymptotically optimal, algorithm for the arc disjoint version in planar directed graphs. Received August 1997; revised January 1999.     

Fast modular exponentiation of large numbers with large exponents

In many problems, modular exponentiation |x^b|_m is a basic computation, often responsible for the overall time performance, as in some cryptosystems, since its implementation requires a large number of multiplications.It is known that |x^b|_m=|x^|b|_(m)|_m for any x in [1,m−1] if m is prime; in this case the number of multiplications depends on (m) instead of depending on b. It was also stated that previous relation holds in the case m=pq, with p and q prime; this case occurs in the RSA method.In this paper it is proved that such a relation holds in general for any x in [1,m−1] when m is a product of any number n of distinct primes and that it does not hold in the other cases for the whole range [1,m−1].Moreover, a general method is given to compute |x^b|_m without any hypothesis on m, for any x in [1,m−1], with a number of modular multiplications not exceeding those required when m is a product of primes.Next, it is shown that representing x in a residue number system (RNS) with proper moduli m_i allows to compute |x^b|_m by n modular exponentiations |x_i^b|_mi in parallel and, in turn, to replace b by |b|_(mi) in the worst case, thus executing a very low number of multiplications, namely log₂m_i for each residue digit.A general architecture is also proposed and evaluated, as a possible implementation of the proposed method for the modular exponentiation.     

<Emphasis Type="Italic">f</Emphasis>-Sensitivity Distance Oracles and Routing Schemes

An f-sensitivity distance oracle for a weighted undirected graph G(V,E) is a data structure capable of answering restricted distance queries between vertex pairs, i.e., calculating distances on a subgraph avoiding some forbidden edges. This paper presents an efficiently constructible f-sensitivity distance oracle that given a triplet (s,t,F), where s and t are vertices and F is a set of forbidden edges such that |F|≤f, returns an estimate of the distance between s and t in G(V,E∖F). For an integer parameter k≥1, the size of the data structure is O(fkn ^1+1/k log (nW)), where W is the heaviest edge in G, the stretch (approximation ratio) of the returned distance is (8k−2)(f+1), and the query time is O(|F|⋅log ² n⋅log log n⋅log log d), where d is the distance between s and t in G(V,E∖F).     

Scheduling DAG-based applications in multicluster environments with background workload using task duplication

Grids or multicluster computing environments are becoming increasingly popular to both scientific and commercial applications. Process scheduling remains a central issue to be effectively resolved in order to exploit the full potential that the grid or multicluster environment can offer. We use a directed acyclic graph (DAG) to model a process or an application where the nodes of the DAG represent the tasks of the process. Prior to the execution of a process in a multicluster environment, the tasks are required to be mapped onto the clusters. In this article, it is shown that the algorithm developed by He et al. [L. He, S.A. Jarvis, D.P. Spooner, D. Bacigalupo, G. Tan, and G.R. Nudd, Mapping DAG-based applications to multiclusters with background workload, Proceedings of the 2005 IEEE International Symposium on Cluster Computing and the Grid, Cardiff, 2005, pp 855–862.] for the multicluster DAG mapping problem can be significantly improved by incorporating the task duplication strategy. The proposed process scheduling algorithm has a time complexity O(| V|²(r+d+1)), where |V| represents the number of tasks; r, the number of clusters; and d, the maximum in-degree of tasks.     

Fast algorithms for minimum matrix norm with application in computer graphics

In this paper we consider the following problem. Given (r ₁,r ₂, ...,r _n) R ⁿ, for anyI= (I ₁,I ₂,...,I _n) Z ⁿ, letE ₁=(e _ij), wheree _ij=(r _i–r_j)–(I _i–I_j), findI Z ⁿ such that |E _I| is minimized, where |·| is a matrix norm. This problem arises from optimal curve rasterization in computer graphics, where minimum distortion of curve dynamic context is sought. Until now, there has been no polynomial-time solution to this computer graphics problem. We present a very simpleO(n lgn)-time algorithm to solve this problem under various matrix norms.This research was supported by the Natural Sciences and Engineering Research Council of Canada under Grant OGP0046373.     

On closed-form approximations for the free energy ofd-dimensional Ising model,II

It has been shown that it is possible to construct families of closed-form approximations lnZ* _d to lnZ _d for the anisotropic Ising model on ad-dimensional hypercubical lattice whose high- and low-temperature series expansions coincide with the corresponding exact expansions up to some order. For the isotropic case the density of zeros ofZ* _d near the critical pointK _c is found under the assumption that they behave like sinh2K=±(sinh2K _c +y±iy). It is shown that there exists a family of closed-form approximations such that ford3 the only possible densities of zeros arem(y)=|y|³ for=0 andm(y)=|y| for 0<||1, i.e., it contains the exact case ford5 corresponding to ||=1.     

Solving Systems of Difference Constraints Incrementally

Difference constraints systems consisting of inequalities of the form x _i - x _j b _i,j occur in many applications, most notably those involving temporal reasoning. Often, it is necessary to maintain a solution to such a system as constraints are added, modified, and deleted. Existing algorithms handle modifications by solving the resulting system anew each time, which is inefficient. The best known algorithm to determine if a system of difference constraints is feasible (i.e., if it has a solution) and to compute a solution runs in Θ (mn) time, where n is the number of variables and m is the number of constraints. This paper presents a new efficient incremental algorithm for maintaining a solution to a system of difference constraints. As constraints are added, modified, or deleted, the algorithm determines if the new system is feasible and updates its solution. When the system becomes infeasible, the algorithm continues to process changes until it becomes feasible again, at which point a feasible solution will be produced. The algorithm processes the addition of a constraint in time O(m + n log n) and the removal of a constraint in constant time when the original system is feasible. More precisely, additions are processed in time O( || Δ || + |Δ| log|Δ| ) , where |Δ| is the number of variables whose values are changed to compute the new feasible solution, and || Δ || is the number of constraints involving the variables whose values are changed. When the original system is infeasible, the algorithm processes any change in O(m + n log n) amortized time. The new algorithm can also be used to check for the existence of negative cycles in dynamic graphs. Received September 25, 1997; revised November 16, 1997.     

Existence and efficient construction of fast Fourier transforms on supersolvable groups

The linear complexityL _K(A) of a matrixA over a fieldK is defined as the minimal number of additions, subtractions and scalar multiplications sufficient to evaluateA at a generic input vector. IfG is a finite group andK a field containing a primitive exp(G)-th root of unity,L _K(G):= min{L _K(A)|A a Fourier transform forKG} is called theK-linear complexity ofG. We show that every supersolvable groupG has amonomial Fourier Transform adapted to a chief series ofG. The proof is constructive and gives rise to an efficient algorithm with running timeO(|G|²log|G|). Moreover, we prove that these Fourier transforms are efficient to evaluate:L _K(G)8.5|G|log|G| for any supersolvable groupG andL _K(G)1.5|G|log|G| for any 2-groupG.     

A Simpler and More Efficient Algorithm for the Next-to-Shortest Path Problem

Given an undirected graph G=(V,E) with positive edge lengths and two vertices s and t, the next-to-shortest path problem is to find an st-path which length is minimum amongst all st-paths strictly longer than the shortest path length. In this paper we show that the problem can be solved in linear time if the distances from s and t to all other vertices are given. Particularly our new algorithm runs in O(|V|log|V|+|E|) time for general graphs, which improves the previous result of O(|V|²) time and takes only linear time for unweighted graphs, planar graphs, and graphs with positive integer edge lengths.     

Efficiently Approximating Polygonal Paths in Three and Higher Dimensions

We present efficient algorithms for solving polygonal-path approximation problems in three and higher dimensions. Given an n -vertex polygonal curve P in \R ^d , d \geq 3 , we approximate P by another poly- gonal curve P' of m ≤ n vertices in \R ^d such that the vertex sequence of P' is an ordered subsequence of the vertices of P . The goal is either to minimize the size m of P' for a given error tolerance \eps (called the min-\# problem), or to minimize the deviation error \eps between P and P' for a given size m of P' (called the min- \eps problem). Our techniques enable us to develop efficient near-quadratic-time algorithms in three dimensions and subcubic-time algorithms in four dimensions for solving the min-\# and min-\eps problems. We discuss extensions of our solutions to d -dimensional space, where d > 4 , and for the L ₁ and L _∈ fty metrics. Received January 10, 1999; revised November 8, 2000.     

Grade of Service Steiner Minimum Trees in the Euclidean Plane

Abstract. Let P = {p ₁ , p ₂ , \ldots, p _n } be a set of n {\lilsf terminal points} in the Euclidean plane, where point p _i has a {\lilsf service request of grade} g(p _i ) ∈ {1, 2, \ldots, n} . Let 0 < c(1) < c(2) < ⋅s < c(n) be n real numbers. The {\lilsf Grade of Service Steiner Minimum Tree (GOSST)} problem asks for a minimum cost network interconnecting point set P and some {\lilsf Steiner points} with a service request of grade 0 such that (1) between each pair of terminal points p _i and p _j there is a path whose minimum grade of service is at least as large as \min(g(p _i ), g(p _j )) ; and (2) the cost of the network is minimum among all interconnecting networks satisfying (1), where the cost of an edge with service of grade g is the product of the Euclidean length of the edge with c(g) . The GOSST problem is a generalization of the Euclidean Steiner minimum tree problem where all terminal points have the same grade of service request. When there are only two (three, respectively) different grades of service request by the terminal points, we present a polynomial time approximation algorithm with performance ratio \frac 4 3 ρ (((5+4\sqrt 2 )/7)ρ , respectively), where ρ is the performance ratio achieved by an approximation algorithm for the Euclidean Steiner minimum tree problem. For the general case, we prove that there exists a GOSST that is the minimum cost network under a full Steiner topology or its degeneracies. A powerful interior-point algorithm is used to find a (1+ε) -approximation to the minimum cost network under a given topology or its degeneracies in O(n ^1.5 (log n + log (1/ε))) time. We also prove a lower bound theorem which enables effective pruning in a branch-and-bound method that partially enumerates the full Steiner topologies in search for a GOSST. We then present a k -optimal heuristic algorithm to compute good solutions when the problem size is too large for the branch-and-bound algorithm. Preliminary computational results are presented.