The traditional, serial, algorithm for finding the strongly connected components in a graph is based on depth first search and has complexity which is linear in the size of the graph. Depth first search is difficult t...
详细信息
The traditional, serial, algorithm for finding the strongly connected components in a graph is based on depth first search and has complexity which is linear in the size of the graph. Depth first search is difficult to parallelize, which creates a need for a different parallel algorithm for this problem. We describe the implementation of a recently proposed parallel algorithm that finds strongly connected components in distributed graphs, and discuss how it is used in a radiation transport solver. (c) 2005 Elsevier Inc. All rights reserved.
We consider all-optical networks with shortest-path routing that use wavelength-division multiplexing and employ wavelength conversion at specific nodes in order to maximize their capacity usage. We present efficient ...
详细信息
We consider all-optical networks with shortest-path routing that use wavelength-division multiplexing and employ wavelength conversion at specific nodes in order to maximize their capacity usage. We present efficient algorithms for deciding whether a placement of wavelength converters allows the network to run at maximum capacity, and for finding an optimal wavelength assignment when such a placement of converters is known. Our algorithms apply to both undirected and directed networks. Furthermore, we show that the problem of designing such networks, i.e., finding an optimal placement of converters, is MAX SNP-hard in both the undirected and the directed case. Finally, we give a linear-time algorithm for finding an optimal placement of converters in undirected triangle-free networks, and show that the problem remains NP-hard in bidirected triangle-free planar networks.
Let G = (V, E) be a connected graph such that each edge e is an element of E and each vertex nu is an element of V are weighted by nonnegative reals w(e) and h(nu), respectively. Let r be a vertex designated as a root...
详细信息
Let G = (V, E) be a connected graph such that each edge e is an element of E and each vertex nu is an element of V are weighted by nonnegative reals w(e) and h(nu), respectively. Let r be a vertex designated as a root, and p be a positive integer. The minmax rooted-subtree cover problem (MRSC) asks to find a partition X = {X-1, X-2,..., X-p} of V and a set of p subtrees T-1, T-2,..., T-p such that each T-i contains X-i boolean OR {r} so as to minimize the maximum cost of the subtrees, where the cost of Ti is defined to be the sum of the weights of edges in T-i and the weights of vertices in X-i. Similarly, the minmax rooted-cycle cover problem (MRCC) asks to find a partition X = {X-1, X-2,..., X-p} of V and a set of p cycles C-1, C-2,..., C-P such that C-i contains X-i boolean OR {r} so as to minimize the maximum cost of the cycles, where the cost of C-i is defined analogously with the MRSC. In this paper, we first propose a (3-2/(p + 1))-approximation algorithm to the MRSC with a general graph G, and we then give a (6-4/(p + 1))-approximation algorithm to the MRCC with a metric (G, w).
Steiner connected dominating set （SCDS） is a generalization of the famous connected dominating set problem, where only a specified set of required vertices has to be dominated by a connected dominating set, and know...
详细信息
Steiner connected dominating set （SCDS） is a generalization of the famous connected dominating set problem, where only a specified set of required vertices has to be dominated by a connected dominating set, and known to be NP- hard. This paper firstly modifies the SCDS algorithm of Guha and Khuller and achieves a worst case approximation ratio of （2 ＋ 1/（m - 1））H（min（△, k）） ＋O（1）, which outperforms the previous best result （c ＋ 1）H（min（△, k）） ＋ O（1） in the case of m ≥ 1 ＋1/（c - 1）, where c is the best approximation ratio for Steiner tree, A is the maximum degree of the graph, k is the cardinality of the set of required vertices, m is an optional integer satisfying 0 ≤ m ≤ min（△, k） and H is the harmonic function. This paper also proposes another approximation algorithm which is based on a greedy approach. The second algorithm can establish a worst case approximation ratio of 2 ln（min（△, k）） ＋ O（1）, which can also be improved to 2 lnk if the optimal solution is greater than c·e^2c＋1/2（c＋1）.
De novo sequencing is one of the most promising proteomics techniques for identification of protein posttranslation modifications (PTMs) in studying protein regulations and functions. We have developed a computer tool...
详细信息
De novo sequencing is one of the most promising proteomics techniques for identification of protein posttranslation modifications (PTMs) in studying protein regulations and functions. We have developed a computer tool PRIME for identification of b and y ions in tandem mass spectra, a key challenging problem in de novo sequencing. PRIME utilizes a feature that ions of the same and different types follow different mass-difference distributions to separate b from y ions correctly. We have formulated the problem as a graph partition problem. A linear integer-programming algorithm has been implemented to solve the graph partition problem rigorously and efficiently. The performance of PRIME has been demonstrated on a large amount of simulated tandem mass spectra derived from Yeast genome and its power of detecting PTMs has been tested on 216 simulated phosphopeptides.
Steiner connected dominating set (SCDS) is a generalization of the famous connected dominating set problem, where only a specified set of required vertices has to be dominated by a connected dominating set, and known ...
详细信息
Steiner connected dominating set (SCDS) is a generalization of the famous connected dominating set problem, where only a specified set of required vertices has to be dominated by a connected dominating set, and known to be NP-hard. This paper firstly modifies the SCDS algorithm of Guha and Khuller and achieves a worst case approximation ratio of (2 + 1/(m - 1))H(min(Delta, k)) + O(1), which outperforms the previous best result (c + 1)H(min(Delta, k)) + O(1) in the case of m >= 1 + 1/(c - 1), where c is the best approximation ratio for Steiner tree, Delta is the maximum degree of the graph, k is the cardinality of the set of required vertices, m, is an optional integer satisfying 0 <= m <= min(Delta, k) and H is the harmonic function. This paper also proposes another approximation algorithm which is based on a greedy approach. The second algorithm can establish a worst case approximation ratio of 21n(min(Delta, k)) + O(1), which can also be improved to 21nk if the optimal solution is greater than c(.)e(2c+1)/2(c+1).
De novo sequencing is one of the most promising proteomics techniques for identification of protein post-translation modifications (PTMs) in studying protein regulations and functions. We have developed a computer too...
详细信息
De novo sequencing is one of the most promising proteomics techniques for identification of protein post-translation modifications (PTMs) in studying protein regulations and functions. We have developed a computer tool PRIME for identification of b and y ions in tandem mass spectra, a key challenging problem in de novo sequencing. PRIME utilizes a feature that ions of the same and different types follow different mass-difference distributions to separate b from y ions correctly. We have formulated the problem as a graph partition problem. A linear integer-programming algorithm has been implemented to solve the graph partition problem rigorously and efficiently. The performance of PRIME has been demonstrated on a large amount of simulated tandem mass spectra derived from Yeast genome and its power of detecting PTMs has been tested on 216 simulated phosphopeptides.
Finding the centerline of the tubular structure helps to segment or analyze the organs such as the vessels or neuron fibers in medical images. This paper described a semi-automatic method using the minimum cost path f...
详细信息
ISBN:
(纸本)0819457213
Finding the centerline of the tubular structure helps to segment or analyze the organs such as the vessels or neuron fibers in medical images. This paper described a semi-automatic method using the minimum cost path finding and Hessian matrix analysis in scale space to calculate the centerline of tubular structure organs. Unlike previous approaches, exhaustive search for tine-like shapes in every scale is prevent. Centerline pixels candidates and the width of the vessel are extracted by analyzing the intensity profile along the gradient vectors in the image. A verification procedure using Hassian matrix analysis with the scale obtained from the gradient analysis is applied to those candidates. Results obtained from the Hessian matrix analysis are used to construct a weighted graph. Finding the minimum cost path in the graph gives the centerline of the tubular structure. The method is applied to find the centerline of the vessels in the 2D angiogram and the neuron fibers in the 3D confocal microscopic images.
In this paper we give a linear algorithm to edge partition a toroidal graph, i.e., graph that can be embedded on the orientable surface of genus one without edge crossing, into three forests plus a set of at most thre...
详细信息
This paper addresses the problem of segmenting an image into regions. We define a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image. We then develo...
详细信息
This paper addresses the problem of segmenting an image into regions. We define a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image. We then develop an efficient segmentation algorithm based on this predicate, and show that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties. We apply the algorithm to image segmentation using two different kinds of local neighborhoods in constructing the graph, and illustrate the results with both real and synthetic images. The algorithm runs in time nearly linear in the number of graph edges and is also fast in practice. An important characteristic of the method is its ability to preserve detail in low-variability image regions while ignoring detail in high-variability regions.
暂无评论