Shoot. Sorry to have missed this.
Hope to catch up on these topics at the Hackathon, or after the conference
tonight. Let me know!
-- Alex
On Wednesday, June 22, 2016 at 5:43:52 PM UTC-4, Josh Day wrote:
>
> Sounds great, I'm in. I'd be especially interested in talking about
> LearnBase.
>
I'm trying to code an efficient implementation of the n-mode tensor
product. Basically, this amounts to taking a dot product between a vector
(b) and the mode-n fibers of a higher-order Array. For example, the mode-3
product of a 4th order array is:
```
C[i,j,k] = dot(A[i,j,:,k], b)
```
After
Hi all,
I'm trying to figure out basic communication patterns for Julia's parallel
computing framework. I'd like to write up a little tutorial when I'm done,
since the documentation on this (in my opinion) is a bit sparse.
The basic question is why this doesn't define `x` on worker #2:
>
> I could make "see [the documentation]" a link
>
I think this ^^^ is a no-brainer. I've been tripped up by the badges in the
past.
On Sunday, August 14, 2016 at 10:41:40 AM UTC-7, Christoph Ortner wrote:
>
> people will get used to the badges quickly enough. I wouldn't worry too
> much.
>
I want to solve a linear system, A*x = b for a fixed A but different b's.
Further, I'd like to overwrite the solution (i.e. x) in b. I found a LAPACK
command that does this:
julia> import Base.LinAlg.LAPACK: gesv!
julia> A = randn(100,100);
julia> B = randn(100);
julia> x1 = A \ B;
julia>
Is there a preferred/standard way to create transposed view of a matrix.
More or less I want something like:
Y = permutedims(X, [2,1])
To return a view into X. Is the `PermutedDimsArray` that Tim Holy was
working on going to be available in Base soon?
Is the answer buried in this thread
