space complexity

This thesis analyzes the time and space complexity for algorithm .

本文分析了查询算法的时间复杂度和空间复杂度。

Performance analyses are developed aim at time and space complexity .

针对探测时间和空间复杂度等指标对算法进行了性能分析。

The space complexity of the new algorithm is O ( 1 ) .

新算法的空间复杂性是O（1）。

Additionally the algorithm has the optimal time and space complexity .

此外，这种算法具有最优的时间和空间复杂性。

The time and the space complexity of the algorithm are lowest .

性能分析表明该算法具有最小的时间复杂度，并且在基于半边Z结构的情况下具有最小的额外空间复杂度。

Methods for Reducing the Space Complexity of the Genera Resolution Principle

降低广义归结原理空间复杂度方法的研究

At last analyze the time complexity and the space complexity of the new algorithms .

最后根据改进，在原有的报文分类算法的时间复杂性和空间复杂性基础上分析了新算法的时间复杂性和空间复杂性。

Its description , time and space complexity and contrasting experiment data are presented .

全文给出了该算法的描述，时间/空间复杂度分析和实验比较数据。

The algorithm we use has low time and space complexity , and has high parallelism .

所用算法具有较低的时间和空间复杂性，并有高度的并行性。

But it is large in time and space complexity , it is restricted in practical .

而动态规划由于其时间和空间复杂度的庞大，在实际使用中受到了限制。

Compared with other algorithms , it has the least time complexity and less space complexity .

与其他二维分类算法相比，该算法具有最小的查找时间复杂度和较小的内存空间复杂度。

Computational complexity is composed of computational time complexity and computational space complexity .

计算复杂度由计算时间复杂度和计算空间复杂度组成。

Fast composite-event probability algorithm with both linear time and space complexity is proposed .

提出了一种可应用于电力系统充裕度分析的快速组合事件概率求取方法。

Corner cutting algorithm is implemented by recursion , which increases the space complexity .

简单割角法在实现的过程中使用了递归，增大了空间复杂度；

However , the time and space complexity of support vector machine is a bottleneck in processing large-scale data .

但由于支持向量机方法复杂，导致在处理大规模数据集时存在相关问题。

All these make the algorithm more efficient . The space complexity of the algorithm is O ( N ) .

该算法的空间复杂度为O（N）；

This largely decreased space complexity .

这样，大大降低了算法的空间复杂度。

Theoretical analyses show that the method has a linear space complexity and converges to the global optimum .

对算法的空间复杂度和收敛性进行了理论分析。

It reduces time and space complexity by introducing data dividing technique , using High Performance computer system such as group system .

这种算法采用数据划分技术，利用高性能计算机系统，如集群式系统，有效地提高了双序列比对的时间空间效率。

How to estimate the time and space complexity is described in order to optimize the performance of the software .

如何估算时间复杂度和空间复杂度，来优化程序的性能；

The algorithm organization is improved . That reduces the space complexity to about 1 / 5 of before .

改进了层次式计算的流程，使算法的空间复杂度降到原算法的1/5左右。

Experimental results show that the algorithm is efficient on the time complexity and the space complexity , but also has good performance .

实验结果表明算法在时间复杂度和空间复杂度是高效的，而且具有较好的性能。

Its time complexity is O ( n2 ) and space complexity is O ( n ) .

它的时间复杂性是O（n~2），空间复杂性是O（n）。

Finally , we analyze the space complexity and time complexity of this algorithm and compare it with previous scheme .

最后分析了改进算法的时间复杂度和空间复杂度，并与同类算法进行了比较。

The analysis and the results of experiments show that the scheme has low time complexity , low space complexity and good security .

实验和分析结果表明，算法的时间复杂度和空间复杂度较低，加密效果较好，安全性较高。

The time complexity and space complexity of the traditional attribute reduction algorithm using discernible matrix are quite big .

传统的利用区分矩阵进行属性约简算法，其时间复杂度和空间复杂度很大。

At last , the analysis and comparison with the quadtree structure was made in both time complexity and space complexity .

最后，从时间复杂度和空间复杂度两个方面与四叉树结构进行分析比较，给出评估结果。

Experiments show that the new algorithm reduces the time and space complexity , especially in the sparse dataset environment .

实验表明新的频繁闭合项集挖掘算法在一定程度上降低了时空复杂度，尤其在稀疏型数据集环境下，该算法所体现的优越性更加突出。

The time complexity and space complexity of the algorithm are all analyzed as O ( nN ~ 2 ) .

分析得出该算法的时间复杂度和空间复杂度均为O（nN2）。

The high space complexity and low efficiency of the standard PPM prediction model affect its application in Web prefetching .

标准PPM预测模型由于存在存储复杂度高、执行效率低等缺点，影响了其推广和应用。