ADAMS solves the model by adopting Lagrange dynamics equation and complementing with rigidity integral algorithm and sparse matrix technology.
ADAMS采用拉格朗日动力学方程，辅以刚性积分算法以及稀疏矩阵技术来求解模型。
This paper introduces the basic idea and algorithm of sparse Matrix multiplication by using incompact storage method.
介绍了对稀疏矩阵进行压缩存储时，稀疏矩阵相乘运算的基本思想和算法。
Using this algorithm, a sparse coefficient matrix which is used to calculate the field component on each node can be obtained.
利用此算法，最后可以得到一个用于计算每个节点场分量的稀疏系数矩阵。
On the basis of analysis on augmentation approach widely used in the multibody dynamic simulation, an improved algorithm based on sparse matrix technique was proposed.
在分析多体动力学仿真计算中广泛使用的增广法基础上，提出了一种基于稀疏矩阵技术的改进算法。
This algorithm used the data of tree structure, and could more reasonably the skipping relations of the non-zero elements in the rows and lines of the sparse check matrix.
该算法采用人们熟悉的树型数据结构，可以较为合理地表示稀疏校验矩阵中行与列中非零元素的跳转关系。
Because the coefficient matrix of the equation to be solved is very sparse, the algorithm with the compact storage scheme is given and the computation cost is also reduced.
对于所求解方程的系数矩阵的高度稀疏性，给出了紧缩存储算法，节省了存储空间和减少了计算量。
In this article, the sparse non-negative matrix factorization algorithm is applied to quantitative predict the mineral resources.
本文主要是论述稀疏非负矩阵分解算法在矿产资源定量预测中的应用研究。
In this article, the sparse non-negative matrix factorization algorithm is applied to quantitative predict the mineral resources.
本文主要是论述稀疏非负矩阵分解算法在矿产资源定量预测中的应用研究。
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