Hi,
I need to compute the solution of least square regression for many times (> 10^6) for large matrix A (>10^9x10^3). I use a hypercube basis, so each row of the matrix A has exactly one non-zero entry with value 1. On different runs, the positions of the 1 are different. I would like to reuse the sparse structure of A, what is the best way to do in this scenario? I have heard of MatCreateMPIAIJWithSplitArrays, but it is kind of difficult to use. I try it a bit and seems like after creation if I need to change the column index, I need to skip the diagonal block. Also, I am not 100% sure, how to find which columns belong to the diagonal block for each process. But as mentioned, I need to run the regression million times, even it is difficult to use, I will try if it is fastest method. Best, Baron
