[SciPy-User] Upcasting of sparse matrix

[Dinesh Vadhia]

This question was asked a few years ago but still not convinced what the right approach is.  Consider a simple binary matrix-vector calculation:

|1 0 1 0| |2.0|
|0 0 1 0| |1.1| = Ax
|0 0 0 1| |4.5|

If A is defined as a dtype=int matrix then during the Ax calculation is it upcast to a float to match x?  The previous answer was that it is upcast and hence, to avoid a performance penalty define A as a dtype=float matrix.

However, it has been pointed out that A's dtype doesn't change, see <https://github.com/scipy/scipy/blob/master/scipy/sparse/compressed.py#L493>, where 'other.data' is not cast before being passed to the wrapped c++ routines.  If so, maybe the blas module performs an automatic upcast or maybe not.

'A' has been tested on large matrices with both dtype=int and dtype=float and no significant timing differences were observed between the two.  The reason for raising the question again is that A with dtype=int uses less memory than with dtype=float.

Would like to understand what is really going on.  Thx!
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