How Does Numpy Multiply Matrices Of Complex Numbers?

I've been trying to figure out the algorithm behind NumPy's matrix multiplication for complex numbers: import numpy as np A = np.array([[17.+0.j, -3.+0.j], [-7.+0.j,

Solution 1:

When you compute A*B it's actually multiplying the matrices elementwise, giving you what is called the hadamard product. It isn't matmul. For example (17.+0.j) * (60.+0.j) = 1020.+0.j, which is the first element in the output. For matrix multiplication use np.dot or simply the @ operator, ie, A@B.

Solution 2:

Found it! It seems NumPy uses np.multiply() (element-wise multiplication), hence the different results.

Here is a naive implementation of this function using for loops:

defnp_multiply(X, Y):
    height = X.shape[0]
    width = X.shape[1]
    output = np.empty((height, width), dtype=np.complex128)

    for i inrange(height):
        for j inrange(width):
            output[i,j] = X[i, j] * Y[i, j]

    return output

This post has an interesting discussion on its performance.