On Tuesday, 13 March 2018 at 03:37:36 UTC, 9il wrote:
[snip]
4. matrix[i] returns a Vec and increase ARC, matrix[i, j]
returns a content of the cell.
I'm not 100% sold on matrix[i] returning a Vec instead of a
1-dimensional matrix. R does something similar and you have to
convert things back to a matrix for some computations more often
than I'd like. If functions can easily take both Mat and Vec
types in a relatively painless fashion, then I wouldn't have an
issue with it.
5. Clever `=` expression based syntax. For example:
// performs CBLAS call of GEMM and does zero memory
allocations
C = alpha * A * B + beta * C;
You might want to explain this in more detail. I saw expression
and my head went to expression templates, but that doesn't seem
to be what you're talking about (overloading opAssign?)
I have a lot of work for next months, but looking for a good
opportunity to make Mat happen.
+1
With respect to the title, the benefit of special matrix types is
when we can call functions (lapack or otherwise) that are
optimized for those types. If you want the best performance for
mir, then I think that's what it would take. I'm not sure how
much you've thought about this. For instance, I understand from
graphics libraries that if you're only working with a particular
size matrix (say 3x3), then you can generate faster code than if
you're working with general matrices.
In addition, performance is not the only thing a new user to mir
would care about They likely would also care about ease-of-use
[1] and documentation. Hopefully these continue to improve.
What's TMMat?
Diag seems like it would be a special case of sparse matrices,
though diag is probably simpler to implement.
[1] Would it be seamless to add a Mat to a Diag? Also what
happens to the api when you add 10 different matrix types and
need to think about all the interactions.
