We give work-optimal and polylogarithmic time parallel algorithms for solving the normalized edit distance problem The normalized edit distance between two strings X and Y with lengths n greater than or equal to m is ...
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ISBN:
(纸本)0818676833
We give work-optimal and polylogarithmic time parallel algorithms for solving the normalized edit distance problem The normalized edit distance between two strings X and Y with lengths n greater than or equal to m is the minimum quotient of the sum of the costs of edit operations transforming X into Y by the length of the edit path corresponding to those edit operations. Marzal and Vidal proposed a sequential algorithm with a time complexity of O(nm(2)). Ne show that this algorithm can be parallelized work-optimally on an array of n (or m) processors, and on a mesh of n x m processors. We then propose a sublinear time algorithm that is almost work-optimal: using O(mn(1.75)) processors, the time complexity of the algorithm is O(n(0.75) log n) and the total number of operations is O(mn(2.5) log n). This algorithm runs on a CREW PRAM, but is likely to work an weaker PRAM models and hypercubes with minor modifications. Finally, we present a polylogarithmic O(log(2) n) time algorithm based an matrix multiplication which runs on a O(n(6)/log n) processor hypercube.
Given a text of length n and a pattern of length m over a finite alphabet Sigma, we consider the string matching (SM) problem with variable length don't cares (VLDCs), where a VLDC (not in Sigma) in the pattern ca...
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Given a text of length n and a pattern of length m over a finite alphabet Sigma, we consider the string matching (SM) problem with variable length don't cares (VLDCs), where a VLDC (not in Sigma) in the pattern can match any substring in the text. This paper presents a first time-optimal parallel SM algorithm with VLDCs. The proposed algorithm can be performed in 0(1) time on an in x n mesh-connected computer with a reconfigurable bus system using O(nm) processors. The previous fastest known parallel SM algorithm takes O(log n) time on the EREW PRAM model with O(nm/log n) processors.
We present a parallel algorithm for solving the minimum weighted completion time scheduling problem for transitive series parallel graphs. The algorithm takes O (log(2) n) time with O (n(3)) processors on a CREW PRAM,...
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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 takes O (log(2) n) time with O (n(3)) processors on a CREW PRAM, where n is the number of vertices of the input graph. This is the first NC algorithm for solving the problem.
In this paper, we address the problem of finding a maximum matching for a convex bipartite graph on a mesh-connected computer (MCC). We shall show that this can be done in optimal time on MCC by designing the efficien...
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In this paper, we address the problem of finding a maximum matching for a convex bipartite graph on a mesh-connected computer (MCC). We shall show that this can be done in optimal time on MCC by designing the efficien...
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This paper presents numerical computations for solving the BMI problem. Four global algorithms including two parallel algorithms are employed to solve the BMI problem by a sequence of concave minimization problems or ...
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This paper presents numerical computations for solving the BMI problem. Four global algorithms including two parallel algorithms are employed to solve the BMI problem by a sequence of concave minimization problems or d.c. programs via concave programming. The parallel algorithms with or based on a suitable partition of an initial enclosing ployhedron are more efficient than the serial ones. Computational experiences are reported for randomly generated BMI problems of small size.
The purpose of this paper is to propose a new parallel algorithm for the knapsack problem. We develop a generation and searching technique to derive the desired two ordered lists in the preliminary process of the gene...
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The purpose of this paper is to propose a new parallel algorithm for the knapsack problem. We develop a generation and searching technique to derive the desired two ordered lists in the preliminary process of the general knapsack problem. The proposed parallel algorithm is developed on the basis of an SIMD machine with shared memory. The algorithm needs O(2n/4) memory and O(2n/8) processors to find a solution for the knapsack problem of n components in time O(2n/2). The proposed parallel algorithm has a cost of O(2(5n/8)) which is an improved result over the past researches.
The n-star graph, denoted by S(n), is one of the graph networks that have been recently proposed as attractive alternatives to the n-cube topology for interconnecting processors in parallel computers. In this paper, w...
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The n-star graph, denoted by S(n), is one of the graph networks that have been recently proposed as attractive alternatives to the n-cube topology for interconnecting processors in parallel computers. In this paper, we present a parallel algorithm for the computation of the Fourier transform on the star graph. The algorithm requires O(n2)multiply-add steps for an input sequence of n! elements, and is hence cost-optimal with respect to the sequential algorithm on which it is based. To the best of our knowledge, this is the first algorithm, and the only one to date, for the computation of the Fourier transform on the star graph.
Horner's algorithm of evaluating a polynomial is studied and formulated as a matrix equationAx= c, with a special *** algorithm proposed by Kowalik and Kumar [5] for solving bidiagonal systems is simplified a...
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Horner's algorithm of evaluating a polynomial is studied and formulated as a matrix equationAx= c, with a special *** algorithm proposed by Kowalik and Kumar [5] for solving bidiagonal systems is simplified and modified by showing that only two stages of three stage algorithm is satisfactory to be used to evaluate polynomials. Some numerical results are presented and discussed. It has been seen that the results are comparable with those of Dorn's [1].
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