This paper presents an O(root kmn polylog(m)) time algorithm for approximate string matching (k-differences problem), in which don't care characters may appear both in a pattern string and in a text string.
This paper presents an O(root kmn polylog(m)) time algorithm for approximate string matching (k-differences problem), in which don't care characters may appear both in a pattern string and in a text string.
Union trees are rooted trees formed in performing a sequence of disjoint-set union operations, where a union rule can be generally described by a function f. In this paper, a polynomial-time algorithm is presented tha...
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Union trees are rooted trees formed in performing a sequence of disjoint-set union operations, where a union rule can be generally described by a function f. In this paper, a polynomial-time algorithm is presented that recognizes f-union trees for any polynomial-time computable monotone function f. Furthermore, a linear-time implementation of the algorithm is given for linear and monotone functions, which includes the two well-known functions - size and rank.
Given a set N = {p1,..., p(n)} of n points in general position in the plane, and a positive integral n-vector d = (d1,...,d(n)) satisfying SIGMA(i=1)n d(i) = 2n-2, can we construct a tree on N, such that the degree of...
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Given a set N = {p1,..., p(n)} of n points in general position in the plane, and a positive integral n-vector d = (d1,...,d(n)) satisfying SIGMA(i=1)n d(i) = 2n-2, can we construct a tree on N, such that the degree of point p(i) is d(i) and none of the (n-1) line segments connecting two points corresponding to endpoints of an edge intersect each other (except possibly at its endpoints)? We give a simple proof of the existence of such a tree in any instance and propose an algorithm polynomial on n for constructing one.
We present a sequential and a parallel algorithm to solve the maximum-weight independent set problem on a permutation graph. Our input data is a permutation pi = [pi1, pi2,...,pi(n)] and the weights of these vertices....
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We present a sequential and a parallel algorithm to solve the maximum-weight independent set problem on a permutation graph. Our input data is a permutation pi = [pi1, pi2,...,pi(n)] and the weights of these vertices. our sequential algorithm takes O(n log log n) time and our parallel algorithm is of O(log2n) time and O(n3/(log n)) processors under the CREW PRAM model.
In this paper, we investigate the optimal assignment problem of cells in PCS (Personal Communication Service) to switches in a wireless ATM network. Given cells and switches on an ATM network (whose locations are fixe...
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In this paper, we investigate the optimal assignment problem of cells in PCS (Personal Communication Service) to switches in a wireless ATM network. Given cells and switches on an ATM network (whose locations are fixed and known), the problem is grouping cells into clusters and assigning these clusters to the switches in an optimum manner. This problem is modeled as a complex integer programming problem and finding an optimal solution of this problem is NP-complete. A three-phase heuristic algorithm MCMLCF (Maximum cell and minimum local communication first) consisting of Cell Pre-Partitioning Phase, Cell Exchanging Phase, and Cell Migrating Phase, is proposed. First, in the Cell Pre-Partitioning Phase, a three-step procedure (Clustering Step, Packing Step, and Assigning Step) is proposed to group cells into clusters. Second, Cell Exchanging Phase, is proposed to greatly improve the result by repeatedly exchanging two cells in different switches. Finally, Cell Migrating Phase is proposed to reduce cost by repeatedly migrating all cells in a used switch to an empty switch. Experimental results indicate that the proposed algorithm runs efficiently. Comparing the results of the algorithm to a naive heuristic called NSF, we have shown that the computation time is reduced by 30.1%. Experimental results show that Cell Exchanging and Cell Migrating phases can reduce the total cost by 34.1% on average. By comparing the results of the proposed algorithm to the genetic algorithm, the heuristic method came close to optimum - on average within 5%.
Chlamtac and Farago have introduced a Transmission Scheduling (TS) algorithm called the (Proper Robust Scheduling) PRS algorithm, which will work for an arbitrary network topology with N stations and maximum degree D ...
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Chlamtac and Farago have introduced a Transmission Scheduling (TS) algorithm called the (Proper Robust Scheduling) PRS algorithm, which will work for an arbitrary network topology with N stations and maximum degree D and produce a schedule of length L = o(N). They use polynomials over GF(q) to achieve this. A generalization of their algorithm, called Multi Level (ML-)PRS is introduced here which makes use of the subfield structure of GF(q) to support multiple classes of users, with different transmission characteristics.
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