[SciPy-User] cholesky of sparse symmetric banded matrix
Tue Aug 21 18:16:29 CDT 2012
I would like to get a cholesky decomposition of a symmetric banded matrix
and multiply it with a dense array
1) is there a way to do linear algebra (dot multiplication) directly in the
"upper diagonal ordered form"?
2) is there an efficient way to go from the "upper diagonal ordered form"
to a sparse diagonal matrix (or both ways)?
Is there code that uses this and that I can look at for the pattern?
my problem is standard linear least squares, where I have an explicit
banded form for the (nobs, nobs) weighting matrix
X'WX and X'Wy
and I need a transformation X2 = W^(0.5) X and y2 = W^(0.5) y
so I get X2'X2 and X2'y2
(nobs: number of observations, prime is transpose)
My first example only has one upper and one lower off-diagonal, so working
with dense is wasteful.
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