An improved algorithm for symbolic computation of Hirota bilinear form of nonlinear equations by a logarithm transformation is presented. The improved algorithm is more efficient by using the property of Hirota-D oper...An improved algorithm for symbolic computation of Hirota bilinear form of nonlinear equations by a logarithm transformation is presented. The improved algorithm is more efficient by using the property of Hirota-D operator. The software package HBFTrans2 is written in Maple and its running efficiency is tested by a variety of soliton equations.展开更多
In order to solve the bottleneck problem of the traditional K-Medoids clustering algorithm facing to deal with massive data information at the time of memory capacity and processing speed of CPU, the paper proposed a ...In order to solve the bottleneck problem of the traditional K-Medoids clustering algorithm facing to deal with massive data information at the time of memory capacity and processing speed of CPU, the paper proposed a parallel algorithm MapReduce programming model based on the research of K-Medoids algorithm. This algorithm increase the computation granularity and reduces the communication cost ratio based on the MapReduce model. The experimental results show that the improved parallel algorithm compared with other algorithms, speedup and operation efficiency is greatly enhanced.展开更多
基金Supported by Scientific Research Fund of Zhejiang Provincial Education Department under Grant No.Y201017148the National Natural Science Foundations of China under Grant No.10735030+2 种基金the Natural Science Fund of Ningbo under Grant No.2009B21003the Scientific Research Fund of Ningbo University under Grant No.XKL09059the K.C.Wong Magana Fund in Ningbo University
文摘An improved algorithm for symbolic computation of Hirota bilinear form of nonlinear equations by a logarithm transformation is presented. The improved algorithm is more efficient by using the property of Hirota-D operator. The software package HBFTrans2 is written in Maple and its running efficiency is tested by a variety of soliton equations.
文摘In order to solve the bottleneck problem of the traditional K-Medoids clustering algorithm facing to deal with massive data information at the time of memory capacity and processing speed of CPU, the paper proposed a parallel algorithm MapReduce programming model based on the research of K-Medoids algorithm. This algorithm increase the computation granularity and reduces the communication cost ratio based on the MapReduce model. The experimental results show that the improved parallel algorithm compared with other algorithms, speedup and operation efficiency is greatly enhanced.
