I've just tried scipy.sparse.linalg.lsqr [*] on the full news20 dataset. On
my box it takes 8 seconds to run with tol=1e-3 and 5 seconds with tol=1e-2
without any accuracy loss. It also solves the memory problem mentioned by
Lars, as it works directly with X and y.
Unlike scipy.linalg.lsqr, scipy.sparse.linalg.lsqr supports a
regularization term so it can actually be used to implement Ridge. Also,
despite the name, it supports dense arrays too so it may be worth comparing
it with solver="dense_cholesky" in the dense case. It cannot be used if
sample_weight != 1.0 though.
I'll try to send a PR some time this week.
Mathieu
[*]
http://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.lsqr.html#scipy.sparse.linalg.lsqr
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