Chang-Biau Yang

Systolic algorithms for the longest common subsequence problem

Abstract The concept of systolic array processors is very suitable for VLSI implementation. In this paper, we propose two systolic algorithms to solve the longest common subsequence problem by dynamic programming approach and also prove that these two algorithms are correct. The order of the time‐processor‐product of our algorithms is equal to that of the corresponding sequential method.

Parallel graph algorithms based upon broadcast communications

Some common guidelines that can be used to design parallel algorithms under the single-channel broadcast communication model are presented. Several graph problems are solved, including topological ordering, the connected component problem, breadth-first search, and depth-first search. If an ideal conflict resolution scheme is used, all of the algorithms require O(n) time by using n processors. Under such a situation, the algorithms are all optimal. If a realistic conflict resolution is used, the algorithms require O(n log n) time by using n/log n processors. For both cases, all of the algorithms achieve optimal speedups. >

Fault tolerance on star graphs

Fault tolerance capability is one of the advantages of multiprocessor systems. We prove that the fault tolerance of star graphs is 2n-5 with restriction to the forbidden faulty set. We propose an algorithm for examining the connectivity of star graphs when 2n-4 faults exist. The algorithm requires O(n/sup 3/ logn) time. We also improve the fault-tolerant routing algorithm proposed by Bagherzadeh et al. (1993) by calculating the cycle structure of a permutation and the avoidance of routing messages to a node without another nonfaulty neighbor. This calculation needs only constant time. We propose an efficient fault-tolerant broadcasting algorithm. When no fault occurs, our broadcasting algorithm remains optimal. The penalty is O(n/sup 2/) if at most n-2 faults exist.<<ETX>>

Efficient algorithms for the longest common subsequence problem with sequential substring constraints

In this paper, we generalize the inclusion constrained longest common subsequence (CLCS) problem to the hybrid CLCS problem which is the combination of the sequence inclusion CLCS and the string inclusion CLCS, called the sequential sub string constrained longest common subsequence (SSCLCS) problem. In the SSCLCS problem, we are given two strings A and B of lengths m and n, respectively, formed by alphabet \Sigma and a constraint sequence C formed by ordered strings (C^1, C^2, C^3, C^l) with total length r. We are to find the longest common subsequence D of A and B containing C^1,C^2,C^3,C^l as sub strings and the order of C¡¦s are retained. This problem have two variants that the strings in C may or may not overlap. We proposed algorithms with O(mnl + (m+ n)(|\Sigma| + r)) and O(mnr + (m+ n)|\Sigma| ) time for the two variants of the problem. For the special case with one or two constraints, our algorithms runs in O(mn+(m+n)(|\Sigma| +r)) and O(mnr +(m+n)j|\Sigma|) time, which are an order faster than the algorithm proposed by Chen and Chao [1].

Genetic algorithms for the investment of the mutual fund with global trend indicator

Our investment strategy for the world mutual funds can be divided into three main parts. First, the global trend indicator (GTI) is defined for evaluating the price change trend of the funds in the future. Then, based on GTI, we derive the monitoring indicator (MI) to measure whether the fund market is in the bull or bear state. Finally, to decide the signal for buying or selling funds, a genetic algorithm is invoked to dynamically select funds according to their past performances (profitability). In our experimental results from January 1999 to December 2008 (10years in total), we achieve the annual profit higher than 10%.

AN ALGORITHM AND APPLICATIONS TO SEQUENCE ALIGNMENT WITH WEIGHTED CONSTRAINTS

Given two sequences S1, S2, and a constrained sequence C, a longest common subsequence of S1, S2 with restriction to C is called a constrained longest common subsequence of S1 and S2 with C. At the same time, an optimal alignment of S1, S2 with restriction to C is called a constrained pairwise sequence alignment of S1 and S2 with C. Previous algorithms have shown that the constrained longest common subsequence problem is a special case of the constrained pairwise sequence alignment problem, and that both of them can be solved in O(rnm) time, where r, n, and m represent the lengths of C, S1, and S2, respectively. In this paper, we extend the definition of constrained pairwise sequence alignment to a more flexible version, called weighted constrained pairwise sequence alignment, in which some constraints might be ignored. We first give an O(rnm)-time algorithm for solving the weighted constrained pairwise sequence alignment problem, then show that our extension can be adopted to solve some constraint-related problems that cannot be solved by previous algorithms for the constrained longest common subsequence problem or the constrained pairwise sequence alignment problem. Therefore, in contrast to previous results, our extension is a new and suitable model for sequence analysis.

Efficient algorithms for finding interleaving relationship between sequences

The longest common subsequence and sequence alignment problems have been studied extensively and they can be regarded as the relationship measurement between sequences. However, most of them treat sequences evenly or consider only two sequences. Recently, with the rise of whole-genome duplication research, the doubly conserved synteny relationship among three sequences should be considered. It is a brand new model to find a merging way for understanding the interleaving relationship of sequences. Here, we define the merged LCS problem for measuring the interleaving relationship among three sequences. An O(n^3) algorithm is first proposed for solving the problem, where n is the sequence length. We further discuss the variant version of this problem with the block information. For the blocked merged LCS problem, we propose an algorithm with time complexity O(n^2m), where m is the number of blocks. An improved O(n^2+nm^2) algorithm is further proposed for the same blocked problem.

Dynamic programming algorithms for the mosaic longest common subsequence problem

The longest common subsequence (LCS) problem can be used to measure the relationship between sequences. In general, the inputs of the LCS problem are two sequences. For finding the relationship between one sequence and a set of sequences, we cannot apply the traditional LCS algorithms immediately. In this paper, we define the mosaic LCS (MLCS) problem of finding a mosaic sequence C, composed of repeatable k sequences in source sequence set S, such that the LCS of C and the target sequence T is maximal. Based on the concept of break points in sequence T, we first propose a divide-and-conquer algorithm with O(n^2m|S|+n^3logk) time for solving this problem, where n and m are the length of T and the maximal length of sequences in S, respectively. Furthermore, an improved algorithm with O(n(m+k)|S|) time is proposed by applying an efficient preprocessing for the MLCS problem.

Multicast Algorithms for Hypercube Multiprocessors

Depending on different switching technologies, the multicast communication problem has been formulated as three different graph theoretical problems: the Steiner tree problem, the multicast tree problem, and the multicast path problem. Our efforts in this paper are to reduce the communication traffic of multicast in hypercube multiprocessors. We propose three heuristic algorithms for the three problem models. Our multicast path algorithm is distributed, our Steiner tree algorithm is centralized, and our multicast tree algorithm is hybrid. Compared with the previous results by simulation, each of our heuristic algorithms improves the communication traffic in the corresponding multicast problem model.

Efficient algorithms for the block edit problems

In this paper, we focus on the edit distance between two given strings where block-edit operations are allowed and better fitting to the human natural edit behaviors. Previous results showed that this problem is NP-hard when block moves are allowed. Various approximations to this problem have been proposed in recent years. However, this problem can be solved by the polynomial-time optimization algorithms if some reasonable restrictions are applied. The restricted variations which we consider involve character insertions, character deletions, block copies and block deletions. In this paper, three problems are defined with different measuring functions, which are P(EIS,C), P(EI,L) and P(EI,N). Then we show that with some preprocessing, the minimum block edit distances of these three problems can be obtained by dynamic programming in O(nm), O(nmlogm) and O(nm^2) time, respectively, where n and m are the lengths of the two input strings.