Please describe the purpose of the new feature or describe the problem to solve.
In the numerical simulation, especailly the reduce order model, we need to deal with a high order sparse tensor. For example, we consider a 4th-order sparse tensor A with shape (100, 100, 200, 200). We multiply A along its 4th axis(l) with a vector B of shape (200,) to obtain a 3rd-order tensor C of shape (100, 100, 200). We do not have appropriate tool to deal with such a high order sparse tensor.
Suggest a solution if possible.
For tensor A, we have coords = [i,j,k,l] and values. we can get C with sparse.COO(newcoords = [i,j,k], newvalues = values*B[l], shape=(A.shape[0], A.shape[1], A.shape[2] ).
If you have tried alternatives, please describe them below.
No response
Additional information that may help us understand your needs.
No response
Please describe the purpose of the new feature or describe the problem to solve.
In the numerical simulation, especailly the reduce order model, we need to deal with a high order sparse tensor. For example, we consider a 4th-order sparse tensor A with shape (100, 100, 200, 200). We multiply A along its 4th axis(l) with a vector B of shape (200,) to obtain a 3rd-order tensor C of shape (100, 100, 200). We do not have appropriate tool to deal with such a high order sparse tensor.
Suggest a solution if possible.
For tensor A, we have coords = [i,j,k,l] and values. we can get C with sparse.COO(newcoords = [i,j,k], newvalues = values*B[l], shape=(A.shape[0], A.shape[1], A.shape[2] ).
If you have tried alternatives, please describe them below.
No response
Additional information that may help us understand your needs.
No response