On Fri, 3 Apr 2009, Ricardo Brazileiro wrote:
i'm refering a graphs, for study models in complex networks. it's
necessary uses matrix....
I don't think anything in Pd handles sparse matrices of any kind, but I
could be wrong. In that case, you will have to use ordinary matrices. If
you have very very large numbers of nodes, it can take a lot of room
(quadratic room) but especially a lot of time (cubic time).
AFAIK, the only tool that has int matrices and byte matrices is GridFlow,
so, anything else would use t_float for that (?), but that only saves on
space.
Probably computation time is the biggest issue. There are some upsides to
using ordinary matrices: for example, you can compute some fabulous
shortcuts using eigendecomposition (valór e vector proprio, aka
autovalores). But if you have some thousand vertices, the computation time
is still quite prohibitive.
So if you need something better, you will need some kind of sparse matrix
or some non-matrix graph representation. If I were you, I'd try to find
something that can be processed as a matrix, so that I have a greater
variety of operations to pick from.
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| Mathieu Bouchard - tél:+1.514.383.3801, Montréal, Québec
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