We show how to reduce the computational time of the practical implementation of the Raviart-Thomas mixed method for second-order elliptic problems. The implementation takes advantage of a recent result which states that certain local subspaces of the vector unknown can be eliminated from the equations by transforming them into stabilization functions; see the paper published online in JJIAM on August 10, 2023. We describe in detail the new implementation (in MATLAB and a laptop with Intel(R) Core (TM) i7-8700 processor which has six cores and hyperthreading) and present numerical results showing 10 to 20% reduction in the computational time for the Raviart-Thomas method of index $k$, with $k$ ranging from 1 to 20, applied to a model problem.

翻译：我们展示了如何减少二阶椭圆问题中Raviart-Thomas混合方法实际实现的计算时间。该实现利用了一项最新结果，该结果指出可通过将向量未知量的某些局部子空间转化为稳定化函数来从方程中消除它们；参见2023年8月10日在线发表于JJIAM的论文。我们详细描述了新实现（基于MATLAB和搭载英特尔酷睿i7-8700处理器、具有六核心及超线程技术的笔记本电脑），并给出了数值结果，表明对于指标$k$从1到20的Raviart-Thomas方法（应用于一个模型问题）的计算时间减少了10%到20%。