Based on this extended directed graph, an algorithm for creating it is proposed, the time complexity analysis of the algorithm is given, and its correctness is proved.
基于这种扩充有向图，提出了一种对象范式生成算法，并给出了算法的时间复杂度分析和正确性证明。
With this method, the reliability to direct the constraint graph is increased, the computation complexity is reduced, and the cyclic constraint graph can be solved expediently.
应用此方法，可提高约束图有向化的可靠性、降低计算复杂度，且能方便地求解带有循环约束的约束图。
This text regards adjacent matrix as the memory structure of graph, and points out how to realize Kruskal algorithm in the computer, and analyses its complexity.
本文以邻接矩阵作为图的存储结构，指出如何在计算机上实现克鲁斯·卡尔算法，并分析所设计算法的时间复杂度。
Introduces the algorithm using dynamic programming to search the shortest distance between two points in graph, and compares the complexity with ordinary method.
本文介绍了利用动态规划法来寻找图中任意两点间最短距离的算法，并将其时间复杂度与一般方法作了比较。
Because of the NP complexity of mapping problem, the thesis studies two-step method: optimal scheduling of data flow graph and optimal processor mapping.
考虑到算法映射问题的NP复杂度和求解时间的指数性增加，本文主要研究两步规划方法，即先进行数据流图的最优规划，然后进行处理器的映射。
The computational complexity of the improved algorithm approaches polynomial complexity, much less than 2 N ( N is the vertex number of a graph).
后者的计算时间复杂性远远低于2N（N为图的顶点数） ，已接近于多项式时间复杂性。
It provides a consistency check method based on graph, which is laconic and easy to handle, and conquers the disadvantage of high formalization and complexity of the old check mechanism.
该方法基于图形化的形式，简洁直观、容易操作，克服了原有检查方法形式化程度高及复杂难操作的缺点。
In this paper, a new data structure of graph, double list, is presented. It is more flexible than other data structures of graph and shows better time and space complexity in graph algorithms.
本文提出了图的一种双链式存储结构，比以往的图的链式存储结构有更好的灵活性，并在图的各种算法的实现上显示了较好的时空复杂性，具有其它存储结构所不具备的各种优点。
However, the performance of LDPC code depends on the girth of its bipartite graph, that is it depends on its sparse parity-check matrices H, especially the complexity of encoding with H.
而LDPC码性能的优劣，与其二分图中是否存在短长度的圈有关，也即与其奇偶校验矩阵的构造有关。
Secondly, we processed the edge expansion and frequent sub-graph isomorphism which are the most complexity parts of frequent subgraphs mining in parallel.
其次，将频繁子图边扩展及同构判断这部分频繁子图挖掘算法中时间复杂度最高的部分并行处理。
Secondly, we processed the edge expansion and frequent sub-graph isomorphism which are the most complexity parts of frequent subgraphs mining in parallel.
其次，将频繁子图边扩展及同构判断这部分频繁子图挖掘算法中时间复杂度最高的部分并行处理。
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