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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link=blue vlink=purple><div class=WordSection1><p class=MsoNormal>Hi,<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>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?<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>I have heard of <span style='color:black'>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.<o:p></o:p></span></p><p class=MsoNormal><span style='color:black'><o:p> </o:p></span></p><p class=MsoNormal><span style='color:black'>Best,<o:p></o:p></span></p><p class=MsoNormal><span style='color:black'>Baron<o:p></o:p></span></p><p class=MsoNormal><o:p> </o:p></p></div></body></html>