coming from a computer graphics and image processing background
i'd love to have a standard way to deal with matrices.
But matrices come in a lot of different flavors, which need
different implementations.
- computer graphics applications need mostly 1d-4d vectors and
matrices.
encapsulation of transformation types:
rigid/similarity/affine/linear/projective give the
opportunity to choose simpler algorithms for e.g. inversion of
a matrix
- images are usually represented as dense 2d matrix (or 3d if
multiple colors channels are present)
- equation systems use dense and sparse matrices, and use lots of
different data structures
to be optimal for different algorithms. yale dok lil coo to
name some sparse formats.
mathematically they have different properties like
square/rectangular positive/negative
definit diagonal
- matlab style matrices
for convenience it should be possible to resize, and
concatenate matrices in different
dimensions, e.g. to compose images or to augment equation
systems
on top of that possibilities to solve those equation systems
would be handy:
with c++ i mostly use eigen, opennl or ceres
this was just a list of things i thought should be kept in mind
when implementing a matrix api - the matrix api doesnt have to
implement all this
imho the matrix api could provide some basic types and a general
interface to access such matrices. for start a simple dense and
sparse n-dimensional matrix format with the possibility to
construct and change matrices. slicing in n-dimensions as simple
as in matlab would be awesome.
such an api would also be a great base for writing graph based
algorithms like markov random fields ...
hopes and wishes :)
best dominik
