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A novel algorithm for all pairs shortest path problem based on matrix multiplication and pulse coupled neural network

机译:基于矩阵乘法和脉冲耦合神经网络的全对最短路径算法

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摘要

All pairs shortest path (APSP) is a classical problem with diverse applications. Traditional algorithms are not suitable for real time applications, so it is necessary to investigate parallel algorithms. This paper presents an improved matrix multiplication method to solve the APSO problem. Afterwards, the pulse coupled neural network (PCNN) is employed to realize the parallel computation. The time complexity of our strategy is only O(log~2 n), where n stands for the number of nodes. It is the fastest parallel algorithm compared to traditional PCNN, MOPCNN, and MPCNN methods.
机译:所有对最短路径(APSP)是具有多种应用的经典问题。传统算法不适合实时应用,因此有必要研究并行算法。本文提出了一种改进的矩阵乘法方法来解决APSO问题。之后,利用脉冲耦合神经网络(PCNN)实现并行计算。我们的策略的时间复杂度仅为O(log〜2 n),其中n表示节点数。与传统的PCNN,MOPCNN和MPCNN方法相比,它是最快的并行算法。

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