一个由矩量法生成的稠密矩阵经过压缩后，可以稀疏存储。
A dense matrix arising from MoM can be stored in sparsity after compressed.
但是在处理高频问题和具有精密结构的缝隙时，对目标剖分过密会导致稠密的阻抗矩阵，未知量增加，影响计算的效率。
But for high frequency problem and complex structure, too dense grid of object will lead to dense impedance matrix and many unknown variables, so the computational efficiency will be very low.
由于BFGS算法产生的矩阵一般是稠密的，因此当它用于求解大规模最优化问题时需采用一定的稀疏技巧。
Because of the density of the matrices produced by the BFGS method generally, it must adopt particular sparse technique for solving large dimensional optimization problems.
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