Sparse matrix ordering is a vital optimization technique often employed for solving large-scale sparse matrices. Its goal is to minimize the matrix bandwidth by reorganizing its rows and columns, thus enhancing efficiency. Conventional methods for algorithm selection usually depend on brute-force search or empirical knowledge, lacking the ability to adjust to diverse sparse matrix structures.As a result, we have introduced a supervised learning-based model for choosing sparse matrix reordering algorithms. This model grasps the correlation between matrix characteristics and commonly utilized reordering algorithms, facilitating the automated and intelligent selection of the suitable sparse matrix reordering algorithm. Experiments conducted on the Florida sparse matrix dataset reveal that our model can accurately predict the optimal reordering algorithm for various matrices, leading to a 55.37% reduction in solution time compared to solely using the AMD reordering algorithm, with an average speedup ratio of 1.45.

翻译：稀疏矩阵排序是求解大规模稀疏矩阵时常用的关键优化技术，其目标是通过重新排列矩阵的行与列来最小化矩阵带宽，从而提高计算效率。传统的算法选择方法通常依赖于暴力搜索或经验知识，缺乏适应不同稀疏矩阵结构的能力。为此，我们提出了一种基于监督学习的稀疏矩阵重排序算法选择模型。该模型能够学习矩阵特征与常用重排序算法之间的关联关系，实现稀疏矩阵重排序算法的自动化智能选择。在佛罗里达稀疏矩阵数据集上的实验表明，我们的模型能够准确预测不同矩阵的最优重排序算法，相较于仅使用AMD重排序算法，求解时间平均减少55.37%，平均加速比达到1.45。