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1.
模糊聚类计算的最佳算法 总被引:14,自引:0,他引:14
给出模糊关系传递闭包在对应模糊图上的几何意义,并提出一个基于图连通分支计算的模糊聚类最佳算法.对任给的n个样本,新算法最坏情况下的时间复杂性函数T(n)满足O(n)≤T(n)≤O(n2).与经典的基于模糊传递闭包计算的模糊聚类算法的O(n3logn)计算时间相比,新算法至少降低了O(n相似文献
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分别在两种重要并行计算模型中给出计算有向基因组排列的反转距离新的并行算法.基于Hannenhalli和Pevzner理论,分3个主要部分设计并行算法:构建断点图、计算断点图中圈数、计算断点图中障碍的数目.在CREW-PRAM模型上,算法使用O(n2)处理器,时间复杂度为O(log2n);在基于流水光总线的可重构线性阵列系统(linear array with a reconfigurable pipelined bus system, LARPBS)模型上,算法使用O(n3)处理器,计算时间复杂度为O(logn). 相似文献
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本文在一个EREW PRAM(exclusive read exclusive write paralled random accessmachine)上提出一个并行快速排序算法,这个算法用k个处理器可将n个项目在平均O((n/k+logn)logn)时间内排序.所以平均来说算法的时间和处理器数量的乘积对任何k≤n/logn是
O(nlogn). 相似文献
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一种高效频繁子图挖掘算法 总被引:11,自引:1,他引:11
由于在频繁项集和频繁序列上取得的成功,数据挖掘技术正在着手解决结构化模式挖掘问题--频繁子图挖掘.诸如化学、生物学、计算机网络和WWW等应用技术都需要挖掘此类模式.提出了一种频繁子图挖掘的新算法.该算法通过对频繁子树的扩展,避免了图挖掘过程中高代价的计算过程.目前最好的频繁子图挖掘算法的时间复杂性是O(n3·2n),其中,n是图集中的频繁边数.提出算法的时间复杂性是O〔2n·n2.5/logn〕,性能提高了O(√n·logn)倍.实验结果也证实了这一理论分析. 相似文献
9.
RNA二级结构预测中动态规划的优化和有效并行 总被引:6,自引:0,他引:6
基于最小自由能模型的方法是计算生物学中RNA二级结构预测的主要方法,而计算最小自由能的动态规划算法需要O(n4)的时间,其中n是RNA序列的长度.目前有两种降低时间复杂度的策略:限制二级结构中内部环的大小不超过k,得到O(n2×k2)算法;Lyngso方法根据环的能量规则,不限制环的大小,在O(n3)的时间内获得近似最优解.通过使用额外的O(n)的空间,计算内部环中的冗余计算大为减少,从而在同样不限制环大小的情况下,在O(n3)的时间内能够获得最优解.然而,优化后的算法仍然非常耗时,通过有效的负载平衡方法,在机群系统上实现并行程序.实验结果表明,并行程序获得了很好的加速比. 相似文献
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区域查询是数据仓库上支持联机分析处理(on-line analytical processing,简称OLAP)的重要操作.近几年,人们提出了一些支持区域查询和数据更新的Cube存储结构.然而这些存储结构的空间复杂性和时间复杂性都很高,难以在实际中使用.为此,提出了一种层次式Cube存储结构HDC(hierarchical data cube)及其上的相关算法.HDC上区域查询的代价和数据更新代价均为O(logdn),综合性能为O((logn)2d)(使用CqCu模型)或O(K(logn)d)(使用Cqnq+Cunu模型).理论分析与实验表明,HDC的区域查询代价、数据更新代价、空间代价以及综合性能都优于目前所有的Cube存储结构. 相似文献
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We present a parallel algorithm for solving the minimum weighted completion time scheduling problem for transitive series parallel graphs. The algorithm takesO(log2
n) time withO(n
3) processors on a CREW PRAM, wheren is the number of vertices of the input graph. This is the first NC algorithm for solving the problem.Research supported in part by NSF Grants CCR-9011214 and CCR-9205982. 相似文献
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Hossam ElGindy 《International journal of parallel programming》1986,15(5):389-398
Previous research on developing parallel triangulation algorithms concentrated on triangulating planar point sets.O(log3
n) running time algorithms usingO(n) processors have been developed in Refs. 1 and 2. Atallah and Goodrich(3) presented a data structure that can be viewed as a parallel analogue of the sequential plane-sweeping paradigm, which can be used to triangulate a planar point set inO(logn loglogn) time usingO(n) processors. Recently Merks(4) described an algorithm for triangulating point sets which runs inO(logn) time usingO(n) processors, and is thus optimal. In this paper we develop a parallel algorithm for triangulating simplicial point sets in arbitrary dimensions based on the idea of the sequential algorithm presented in Ref. 5. The algorithm runs inO(log2
n) time usingO(n/logn) processors. The algorithm hasO(n logn) as the product of the running time and the number of processors; i.e., an optimal speed-up. 相似文献
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This paper determines upper bounds on the expected time complexity for a variety of parallel algorithms for undirected and directed random graph problems. For connectivity, biconnectivity, transitive closure, minimum spanning trees, and all pairs minimum cost paths, we prove the expected time to beO(log logn) for the CRCW PRAM (this parallel RAM machine allows resolution of write conflicts) andO(logn · log logn) for the CREW PRAM (which allows simultaneous reads but not simultaneous writes). We also show that the problem of graph isomorphism has expected parallel timeO(log logn) for the CRCW PRAM andO(logn) for the CREW PRAM. Most of these results follow because of upper bounds on the mean depth of a graph, derived in this paper, for more general graphs than was known before.For undirected connectivity especially, we present a new probabilistic algorithm which runs on a randomized input and has an expected running time ofO(log logn) on the CRCW PRAM, withO(n) expected number of processors only.Our results also improve known upper bounds on the expected space required for sequential graph algorithms. For example, we show that the problems of finding connected components, transitive closure, minimum spanning trees, and minimum cost paths have expected sequential spaceO(logn · log logn) on a deterministic Turing Machine. We use a simulation of the CRCW PRAM to get these expected sequential space bounds.This research was supported by National Science Foundation Grant DCR-85-03251 and Office of Naval Research Contract N00014-80-C-0647.This research was partially supported by the National Science Foundation Grants MCS-83-00630, DCR-8503497, by the Greek Ministry of Research and Technology, and by the ESPRIT Basic Research Actions Project ALCOM. 相似文献
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Xin He 《Algorithmica》1990,5(1):545-559
We present an efficient algorithm for 4-coloring perfect planar graphs. The best previously known algorithm for this problem takesO(n
3/2) sequential time, orO(log4
n) parallel time withO(n3) processors. The sequential implementation of our algorithm takesO(n logn) time. The parallel implementation of our algorithm takesO(log3
n) time withO(n) processors on a PRAM. 相似文献
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This paper determines upper bounds on the expected time complexity for a variety of parallel algorithms for undirected and directed random graph problems. For connectivity, biconnectivity, transitive closure, minimum spanning trees, and all pairs minimum cost paths, we prove the expected time to beO(log logn) for the CRCW PRAM (this parallel RAM machine allows resolution of write conflicts) andO(logn · log logn) for the CREW PRAM (which allows simultaneous reads but not simultaneous writes). We also show that the problem of graph isomorphism has expected parallel timeO(log logn) for the CRCW PRAM andO(logn) for the CREW PRAM. Most of these results follow because of upper bounds on the mean depth of a graph, derived in this paper, for more general graphs than was known before. For undirected connectivity especially, we present a new probabilistic algorithm which runs on a randomized input and has an expected running time ofO(log logn) on the CRCW PRAM, withO(n) expected number of processors only. Our results also improve known upper bounds on the expected space required for sequential graph algorithms. For example, we show that the problems of finding connected components, transitive closure, minimum spanning trees, and minimum cost paths have expected sequential spaceO(logn · log logn) on a deterministic Turing Machine. We use a simulation of the CRCW PRAM to get these expected sequential space bounds. 相似文献
16.
A Fast Algorithm for Image Component Labeling with Local Operators on Mesh Connected Computers 总被引:1,自引:0,他引:1
A new parallel algorithm for image component labeling with local operators on SIMD mesh connected computers is presented. This algorithm provides a positive answer to the open question of whether there exists an O(n)-time and O(log n)-space local labeling algorithm on SIMD mesh connected computers. The algorithm uses a pipeline mechanism with stack-like data structures to achieve the lower bound of O(n) in time complexity and O(log n) in space complexity. Additionally, the algorithm has very small multiplicative constants in its complexities by using local parallel-shrink and label-propagate operations. 相似文献
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We present an efficient algorithm for 4-coloring perfect planar graphs. The best previously known algorithm for this problem takesO(n 3/2) sequential time, orO(log4 n) parallel time withO(n3) processors. The sequential implementation of our algorithm takesO(n logn) time. The parallel implementation of our algorithm takesO(log3 n) time withO(n) processors on a PRAM.
相似文献18.
《国际计算机数学杂志》2012,89(2):297-317
Two algorithms for shortest path problems are presented. One is to find the all-pairs shortest paths (APSP) that runs in O(n 2logn + nm) time for n-vertex m-edge directed graphs consisting of strongly connected components with O(logn) edges among them. The other is to find the single-source shortest paths (SSSP) that runs in O(n) time for graphs reducible to the trivial graph by some simple transformations. These algorithms are optimally fast for some special classes of graphs in the sense that the former achieves O(n 2) which is a lower bound of the time necessary to find APSP, and that the latter achieves O(n) which is a lower bound of the time necessary to find SSSP. The latter can be used to find APSP, also achieving the running time O(n 2). 相似文献
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1 IntroductionLet G = (V, E) be a connected, undirected graph with a weight function W on the set Eof edges to the set of reals. A spanning tree is a subgraph T = (V, ET), ET G E, of C suchthat T is a tree. The weight W(T) of a spanning tree T is the sum of the weights of its edges.A spanning tree with the smallest possible'weight is called a minimum spanning tree (MST)of G. Computing an MST of a given weighted graph is an important problem that arisesin many applications. For this … 相似文献
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In this paper,a sequential algorithm computing the aww vertex pair distance matrix D and the path matrix Pis given.On a PRAM EREW model with p,1≤p≤n^2,processors,a parallel version of the sequential algorithm is shown.This method can also be used to get a parallel algorithm to compute transitive closure array A^* of an undirected graph.The time complexity of the parallel algorithm is O(n^3/p).If D,P and A^* are known,it is shown that the problems to find all connected components,to compute the diameter of an undirected graph,to determine the center of a directed graph and to search for a directed cycle with the minimum(maximum)length in a directed graph can all be solved in O(n^2/p logp)time. 相似文献