1) large sparse problems
大型稀疏问题
1.
The most popular methods to solve large sparse problems are based on projection techniques on ap-propriate subspaces.
求解大型稀疏问题最流行的方法是建立在子空间投影技术之上的。
2) sparsity problems
稀疏性问题
1.
To solve the sparsity problems of recommender systems,a recommendation method is designed by means of the combination of the non-neighbouring sequential pattern mining algorithms and the item-based Collaborative Filtering(CF) recommendation algorithms.
针对推荐系统存在的稀疏性问题,提出将非邻近序列模式挖掘算法与基于项目的协作过滤推荐算法相结合的推荐方法,通过构造Markov概率的路径加权转移矩阵,计算资源被推荐的可能性,向用户进行推荐。
3) large sparse
大型稀疏
1.
Present the concept of equivalent linear programming problem, which characterizes the property of the linear programming problem, constructs a algorithm with similar partial pivot for solving the linear programming problem, which convenience solving large sparse linear programming problem.
这种算法适用于大型稀疏线性规划问题 ,且算法是稳定的 ,不必添加任何的辅助问题和松弛变量 。
5) large-scale problems
大型问题
1.
This paper introduces a method of using MATLAB for solving large-scale problems,based on the feature of the large computing and saving brought by methods of gradient and imitational Newton in engineering and scientific computing.
针对工程和科学计算中解决大型问题所采用的梯度法和拟定Newton法计算量大和存储量大的特点,引入了MATLAB来求解大型问题。
补充资料：鞍山钢铁(集团)公司大型轧钢厂大型轧机热轧型钢
鞍山钢铁(集团)公司大型轧钢厂大型轧机热轧型钢
鞍山钢铁(集团)公司大型轧钢厂大型轧机热轧型钢
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条