Hey, in the context of linear regression/OLS, using Julia 0.4 on Mac OSX 10.9, x\y
returns ERROR: argument matrix must be square in lufact at linalg/umfpack.jl:110 in factorize at linalg/cholmod.jl:1047 Indeed, x is a sparse, rectangular matrix, approx. 100000x1000. y is a dense matrix of 100000x30, although I would be satisfied solving only one line at a time. I have used similar (albeit larger and even sparser) data in MATLAB and the solution is very fast, on the order of seconds. Can I use the backlash with sparse rectangular matrices? Also, the same x will probably be used in more equations in the future, so I am considering storing a factorization. However, chol(x) very quickly fills up all available memory (12GB). If it matters, x is highly collinear. (If it isn't obvious, I do not have much experience with linear algebra, all of this is very new to me.) Thanks, Jona
