Hi Rowan,
i would like to share some thoughts of mine with you.
First of all some history. As you have correctly noticed, LAPACK
was a build
requirement for GNU APL up to including version 1.4. Looking at
the
configure.ac in 1.4:
Hi again,
sorry, but I have to correct myself: it was actually Peter Teeson
who
pointed us to the ACM papers.
Best Regards,
Jürgen
On 4/13/20 1:17 PM, Dr. Jürgen
Sauermann wrote:
Hi Rowan,
"I added a
new primitive in GNU APL which computes the QR factorization of a real
or complex matrix"
Looking through the source code I see the following in `src/LApack.hh`:
```
// Compute QR factorization with column pivoting of A:
// A * P = Q * R
//
char * pivot_tau_tmp = new char[N*(si
Hi Rowan,
see below.
On 4/13/20 4:57 PM, Rowan Cannaday
wrote:
"I
added a
new primitive in GNU APL which computes the QR
factorization of a real
This is fantastic, I was not aware you had added an axis operator to
monadic ⌹.
Will mess around with this when I've got time.
Thanks again,
- Rowan
On Mon, Apr 13, 2020 at 3:48 PM Dr. Jürgen Sauermann <
mail@jürgen-sauermann.de> wrote:
> Hi Rowan,
>
> see below.
>
>
> On 4/13/20 4:57 PM, Rowa
The paper you referred to was a huge epiphany for me.
Having previously worked in the business world using COBOL and FORTRAN
the beauty and elegance of APL blew me away. It still does.
At IPSA we used to model proposed changes (mostly algorithms) to the
interpreter in APL first.
And then wrot
Here is a quick solution to the eigenvalue problem. Should be refined and
extended to calculate eigenvectors.
{0~⍨∈⍵×∘.=⍨⍳≢⍵}({r+.×⍉qt⊣(qt r)←⌹[⎕CT]⍵ ;qt;r}⍣{(|⍺)≡|⍵}) 3 3 ⍴ ⍳9
16.11684397 ¯1.11684397 ¯3.625973215E¯16
As a quick note, my ⎕CT was 1E¯13, however it doesn't seem to zero out the
Another deficiency I've noticed in my above solution is that it does not
converge for complex eigenvalues.
- Rowan
On Mon, Apr 13, 2020 at 6:07 PM Rowan Cannaday wrote:
> Here is a quick solution to the eigenvalue problem. Should be refined and
> extended to calculate eigenvectors.
>
> {0~⍨∈⍵×∘
I was reading the spec on outer join to see if I could finally fully
understand how it works.
For reference, the spec refers to the syntax as follows:
X a.b Y
The function a is referenced twice in the description for this function,
both times in the form a/ (i.e. with the reduce operator app
