如何将Eigen :: SparseMatrix包装在超过3标准压缩行/ colum数组 [英] How to wrap Eigen::SparseMatrix over preexistant 3-standard compress row/colum arrays
问题描述
注意：我已经提出这个问题，但它被关闭，因为太广泛没有多少解释。我不能看到这个问题可能更具体（它处理特定类的一个特定的库的具体用法...），所以我认为它是一个像主持人的错误，并再次问。 ..
NOTE: I allready asked this question, but it was closed because of "too broad" without much explanation. I can't see how this question could be more specific (it deals with a specific class of a specific library for a specific usage...), so I assume that it was something like a "moderator's mistake" and ask it again...
我想使用Eigen对稀疏矩阵进行稀疏矩阵/矩阵乘法。这些矩阵已经在我在标准3阵列压缩行/列strorage中工作的代码中定义。
I would like to perfom sparse matrix/matrix multiplication using Eigen on sparse matrices. These matrices are already defined in the code I am working on in standard 3-arrays compressed row/column strorage.
然后我想使用Eigen :: SparseMatrix类作为这些数组上的包装器（假定内部使用这种3阵列存储器），以避免在存储器中复制矩阵。我想做如下：
Then I would like to use the Eigen::SparseMatrix class as a wrapper on these arrays (assuming that internally Eigen uses such a 3-arrays storage) in order to avoid to duplicate matrices in memory. I would like to do something like the following:
Eigen::SparseMatrix smin0(n,m);
Eigen::SparseMatrix smin1(m,l);
Eigen::SparseMatrix smout(n,l);
smin0.set_innerPtr(myInnerPtr0);
smin0.set_outerPtr(myOuterPtr0);
smin0.set_valuePtr(myValuePtr0);
smin1.set_innerPtr(myInnerPtr1);
smin1.set_outerPtr(myOuterPtr1);
smin1.set_valuePtr(myValuePtr1);
smout=smin0*smin1;
int *myOutInnerPtr=smout.innerIndexPtr();
int *myOutOuterPtr=smout.outerIndexPtr();
double *myOutValuePtr=smout.valuePtr();
有可能吗？如果是，如何？
Is it possible and if yes, how?
很感谢
推荐答案
ggael指出，您可以使用 Eigen :: MappedSparseMatrix 。
As ggael pointed out, you can use Eigen::MappedSparseMatrix for that.
之所以不能覆盖 SparseMatrix 的内部指针是当 SparseMatrix 重新分配它们时会导致问题，方式然后Eigen（以及如何Eigen内部分配内存是一个实现细节，你不应该真正依赖在你的代码）。
The reason you can't just overwrite the internal pointers of a SparseMatrix is that this would cause problems when the SparseMatrix deallocates them, but you allocated them in a different way then Eigen does (and how Eigen internally allocates memory is an implementation detail you should not really rely on in your code).
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