Multiply matrix by vector row-wise

[nilgoyette]

How can I multiply a matrix and a vector "row-wise" in nalgebra? Like I would do in numpy

>>> m = np.eye(3)
>>> v = np.array([1, 2, 3])
>>> m * v  # Or v * m
# Gives [[1, 0, 0],
#        [0, 2, 0],
#        [0, 0, 3]]

I understand that I can convert my vector into a square matrix, then use * (or dot) or code a generic function that does this task but multiplying each row with a number will be faster.

I thought there would be be a method/operator that does it, so I asked this question on the r/rust "simple question" post and on SO. It seems that such a method doesn't exist. Can you please confirm?

Moreover, if this is right, should we add this method, and what would be its name?

[tpdickso]

Can you elaborate on how you want this operation to work? The example you've given doesn't seem quite correct, as far as I can tell; m * v should produce the vector [1, 2, 3]. I'm not sure if having a built-in function would be faster than rolling your own, either, as:

m.row_iter_mut().zip(v.iter()).for_each(|(row, component)| {
    row *= component;
})

should likely do roughly the same thing execution-wise as a built-in method would do; do you have a use-case for this for which this approach benchmarks poorly?

Can you elaborate on the use-case for this per-row componentwise multiplication? This could be a built-in method on Matrix, but there are unfortunately diminishing returns on adding specialized methods; when there are too many methods, they aren't very discoverable and users may end up reaching for the row_iter_mut() approach anyway. So if this is a common operation with a well-understood name then that could strengthen the case for having it as its own method on Matrix, but if it would end up being named something like row_component_mul{_assign} or something then it might end up adding more API surface area than it ends up saving time for downstream users.

[nilgoyette]

I have a 3x3 transformation matrix m and a spacing vector (the resolution of the voxel) v. I need to apply the spacing to the matrix, so I thought I could do v * m. I'm not a mathematician myself and I'm not aware if programmers seldomly or frequently do that operation. I only know that numpy does it.

I agree with both your points:

• I can roll my own
• not very discoverable to add a new method

I was just asking if it's a good idea. However, now that I think about it more. There's no need to add a method. This operation could use the * operator, like in numpy. As I said, I'm no mathematician... Is it "wrong" to define the behavior of matrix * vector and vector * matrix? Should it be forbidden like it is now?