Let G(s) = (V-s, E-s) be a simple connected graph. A vertex v is an element of V-s is an articulation vertex if deletion of v and its incident edges from G(s) disconnects the graph into at least two connected componen...
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Let G(s) = (V-s, E-s) be a simple connected graph. A vertex v is an element of V-s is an articulation vertex if deletion of v and its incident edges from G(s) disconnects the graph into at least two connected components. Finding all articulation vertices of a given graph is called the articulation vertex problem. A vertex u is an element of V-s is called a hinge vertex if there exist any two vertices x and y in G(s) whose distance increase when u is removed. Finding all hinge vertices of a given graph is called the hinge vertex problem. These problems can be applied to improve the stability and robustness of communication network systems. In this paper, we propose linear time algorithms for the articulation vertex problem and the hinge vertex problem of circular permutation graphs.
We design a new label shortest path algorithm by applying the concept of a pseudo permanent label. This approach allows an algorithm to partition the set of nodes into two new sets: pseudo permanently labeled nodes an...
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We design a new label shortest path algorithm by applying the concept of a pseudo permanent label. This approach allows an algorithm to partition the set of nodes into two new sets: pseudo permanently labeled nodes and its complementary set. From this point of view, this new label method can be considered as a label setting method. Moreover, at least one node becomes permanently labeled when some nodes which belong to the set of pseudo permanently labeled nodes are scanned in each iteration of the algorithm. In the case of networks with non-negative length arcs it is easy to prove that this node has the minimum distance label among the non-pseudo permanently labeled nodes. On the other hand, it is not known during the computation which pseudo permanently labeled nodes are permanently labeled. Therefore, all distance labels are temporary and the algorithm becomes a label correcting method. Nevertheless, the proposed algorithm exhibits some nice features, such as: (1) the time bound for the running of the algorithm for a network with n nodes and m arcs is O(nm);(2) the number of node scan operations in the algorithm is less than the number of these operations in the previous label correcting algorithms as is observed in the computational experience;(3) the algorithm incorporates two new rules which allow easy detection of a negative cycle in the network;(4) the algorithm is quite simple and very easy to implement, and does not require sophisticated data structures;(5) the algorithm exhibits flexibility in the order in which the new pseudo permanently labeled nodes are scanned. The above features are possible through the application of the pseudo permanent label concept.
The Pattern Matching problem with Swaps consists in finding all occurrences of a pattern P in a text T, when disjoint local swaps in the pattern are allowed. In the Approximate Pattern Matching problem with Swaps one ...
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The Pattern Matching problem with Swaps consists in finding all occurrences of a pattern P in a text T, when disjoint local swaps in the pattern are allowed. In the Approximate Pattern Matching problem with Swaps one seeks, for every text location with a swapped match of P, the number of swaps necessary to obtain a match at the location. In this paper we devise two general algorithms for both Standard and Approximate Pattern Matching with Swaps, named CROSS-SAMPLING and BACKWARD-CROSS-SAMPLING, with a O(nm) and O(nm(2)) worst-case time complexity, respectively. Then we provide efficient implementations of them, based on bit-parallelism, which achieve O(n) and O(nm) worst-case time complexity, with patterns v-hose length is comparable to the word-size of the target machine. From an extensive comparison with. some of the most Live algorithms for the matching problem, it turns out that our algorithms a) very flexible and achieve very good results in practice.
In an undirected graph, the feedback vertex set (FVS for short) problem is to find a set of vertices of minimum cardinality whose removal makes the graph acyclic. The FVS has applications to several areas such that co...
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In an undirected graph, the feedback vertex set (FVS for short) problem is to find a set of vertices of minimum cardinality whose removal makes the graph acyclic. The FVS has applications to several areas such that combinatorial circuit design, synchronous systems, computer systems, VLSI circuits and so on. The FVS problem is known to be NP-hard on general graphs but interesting polynomial solutions have been found for some special classes of graphs. In this paper, we present an 0(n(2.68) + gamma n) time algorithm for solving the FVS problem on trapezoid graphs, where gamma is the total number of factors included in all maximal cliques.
The independent spanning trees (ISTs) problem attempts to construct a set of pairwise independent spanning trees and it has numerous applications in networks such as data broadcasting, scattering and reliable communic...
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The independent spanning trees (ISTs) problem attempts to construct a set of pairwise independent spanning trees and it has numerous applications in networks such as data broadcasting, scattering and reliable communication protocols. The well-known ISTs conjecture, Vertex/Edge Conjecture, states that any n-connected/n-edge-connected graph has n vertex-ISTs/edge-ISTs rooted at an arbitrary vertex r. It has been shown that the Vertex Conjecture implies the Edge Conjecture. In this paper, we consider the independent spanning trees problem on the n-dimensional locally twisted cube LTQ(n). The very recent algorithm proposed by Hsieh and Tu (2009) [12] is designed to construct n edge-ISTs rooted at vertex 0 for LTQ(n). However, we find out that LTQ(n) is not vertex-transitive when n >= 4;therefore Hsieh and Tu's result does not solve the Edge Conjecture for LTQ(n),,. In this paper, we propose an algorithm for constructing n vertex-ISTs for LTQ(n);consequently, we confirm the Vertex Conjecture (and hence also the Edge Conjecture) for LTQ(n),. (C) 2011 Elsevier B.V. All rights reserved.
Given an alphabet I ={1,2 pound,aEuro broken vertical bar,|I |} pound text string TaI pound (n) and a pattern string PaI pound (m) , for each i=1,2,aEuro broken vertical bar,n-m+1 define L (p) (i) as the p-norm distan...
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Given an alphabet I ={1,2 pound,aEuro broken vertical bar,|I |} pound text string TaI pound (n) and a pattern string PaI pound (m) , for each i=1,2,aEuro broken vertical bar,n-m+1 define L (p) (i) as the p-norm distance when the pattern is aligned below the text and starts at position i of the text. The problem of pattern matching with L (p) distance is to compute L (p) (i) for every i=1,2,aEuro broken vertical bar,n-m+1. We discuss the problem for d=1,2,a. First, in the case of L (1) matching (pattern matching with an L (1) distance) we show a reduction of the string matching with mismatches problem to the L (1) matching problem and we present an algorithm that approximates the L (1) matching up to a factor of 1+epsilon, which has an O (1/epsilon 2nlogm log vertical bar Sigma vertical bar) run time. Then, the L (2) matching problem (pattern matching with an L (2) distance) is solved with a simple O(nlog m) time algorithm. Finally, we provide an algorithm that approximates the L (a) matching up to a factor of 1+epsilon with a run time of O (1/epsilon 2nlogm log vertical bar Sigma vertical bar). We also generalize the problem of String Matching with mismatches to have weighted mismatches and present an O(nlog (4) m) algorithm that approximates the results of this problem up to a factor of O(log m) in the case that the weight function is a metric.
Image matching under rotation is a computational problem to determine for two given images A and B a rotation of A that most accurately resembles B. The research in combinatorial pattern matching led to a series of im...
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Image matching under rotation is a computational problem to determine for two given images A and B a rotation of A that most accurately resembles B. The research in combinatorial pattern matching led to a series of improved algorithms which commonly solve this problem by a sophisticated search in the set of all rotations of A. This paper provides the lower bound Omega(n3) on the worst case cardinality of this set for images of size n x n and presents the first optimal algorithm of such kind, i.e., one that solves image matching under rotations in time Omega(n(3)). Moreover, for image matching under compositions of rotation and scaling a new lower bound Omega(n(6)/log n) on the worst case cardinality of the set of rotated and scaled transformations of an n x n image is provided. This bound almost matches the upper bound Omega(n(6)). (C) 2010 Elsevier B.V. All rights reserved.
Given a tree T = (V, E) of n nodes such that each node v is associated with a value-weight pair (val(v), w(v)), where value val(v) is a real number and weight w(v) is a non-negative integer, the density of T is define...
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Given a tree T = (V, E) of n nodes such that each node v is associated with a value-weight pair (val(v), w(v)), where value val(v) is a real number and weight w(v) is a non-negative integer, the density of T is defined as Sigma(v is an element of V) val(v)/Sigma(v is an element of V) w(v). A subtree of T is a connected subgraph (V', E') of T, where V' subset of V and E' subset of E. Given two integers w(min) and w(max), the weight-constrained maximum-density subtree problem on T is to find a maximum-density subtree T' = (V', E') satisfying w(min) <= Sigma(v is an element of V')w(v) <= W-max. In this paper, we first present an O(w(max)n)-time algorithm to find a weight-constrained maximum-density path in a tree T, and then present an O (w(max)(2)n)-time algorithm to find a weight-constrained maximum-density subtree in T. Finally, given a node subset S subset of V, we also present an O(w(max)(2)n)-time algorithm to find a weight-constrained maximum-density subtree in T which covers all the nodes in S.
We consider the complexity of computing a longest increasing subsequence (LIS) parameterised by the length of the output. Namely, we show that the maximal length k of an increasing subsequence of a permutation of the ...
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We consider the complexity of computing a longest increasing subsequence (LIS) parameterised by the length of the output. Namely, we show that the maximal length k of an increasing subsequence of a permutation of the set of integers {1, 2, ..., n) can be computed in time O(n log log k) in the RAM model, improving the previous 30-year bound of O(n log k). The algorithm also improves on the previous O(n log log n) bound. The optimality of the new bound is an open question. Reducing the computation of a longest common subsequence (LCS) between two strings to an LIS computation leads to a simple O(n log log n)-time algorithm for two sequences having r pairs of matching symbols and an LCS of length k. Crown Copyright (C) 2010 Published by Elsevier Inc. All rights reserved.
Boundary labeling is a relatively new labeling method. It can be useful in automating the production of technical drawings and medical drawings, where it is common to explain certain parts of the drawing with text lab...
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Boundary labeling is a relatively new labeling method. It can be useful in automating the production of technical drawings and medical drawings, where it is common to explain certain parts of the drawing with text labels, arranged on its boundary so that other parts of the drawing are not obscured. In boundary labeling, we are given a rectangle R which encloses a set of n sites. Each site s is associated with an axis-parallel rectangular label l(s). The labels must be placed in distinct positions on the boundary of R and they must be connected to their corresponding sites with polygonal lines, called leaders, so that the labels are pairwise disjoint and the leaders do not intersect each other. In this paper, we study a version of the boundary labeling problem where the sites can 'float' within a polygonal region. We present a polynomial time algorithm, which runs in O(n(3)) time and produces a labeling of minimum total leader length for labels of uniform size placed in fixed positions on the boundary of rectangle R.
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