Both svd and gesvd return a 3 element box vector, but the shapes of array
inside box are different. Do their results agree with each other?

I'll take a look at the bug in j  lapack.
On Aug 14, 2014 8:12 AM, "Scott Locklin" <[email protected]> wrote:

> I noticed that the c-lapack svd function is very slow and memory
> inefficient on my (linux) laptop. The native J version in math/misc/svd is
> actually many times faster and more memory efficient, so I used
> imath/misc/svd instead. Now, this flags a bug in C-lapack for certain, as
> it isn't so bad when i do the same thing in R, but this also shows the
> amazing power of J.
>
> As an addendum, I noticed that R no longer uses dgesvd; they use dgesdd
> instead, as I guess it is more efficient. Also, both gesvd and native svd
> techniques do produce the same answer.
>
> trn=. 250000 30 $?.1e6#0
>
> load'math/misc/svd'
> load'math/lapack'
> load'math/lapack/gesvd'
>
> ts=: 6!:2, 7!:2@]
> b=.ts 'q2=:svd 10000{.trn'
> a=.ts 'q=:gesvd_jlapack_ 10000{.trn'
>
> a%b
> 29.1044 171.825
>
> If you want to see what I am using SVD for, I've been fiddling with matrix
> approximants, CUR decomposition in particular:
> https://github.com/locklin/jCUR
> CUR decomposition is a 2009 technique for efficiently approximating
> matrices by selecting  quasi-random pieces of the original matrix. It has
> utility in dimensionality reduction in the same spirit as PCA, but it is a
> more interpretable, since you have original rows and columns of the matrix.
> Such things might eventually be incorporated into Jd as a way of making
> sense of large amounts of data. There are other such techniques I plan on
> looking at eventually, but I want to find a good use case for CUR first
> (probably something in portfolio theory).
>
> -SL
>
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