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Matrix Multiplication in Eigen or Scipy

I’m trying to optimize a matrix multiplication function using Numpy and Eigen.
Usually when I’m coding in Python or C++ I use Scipy or Eigen, but this time I have to use Numpy and I don’t know how to do it.
I need to divide two matrices that it’s the size of a vector and after I need to multiply both and I have to use the elements of the vector in a specific order.
For example the algorithm for multiply a vector x of size L to a 2 x 2 matrix A is:
for i = 0 to L-1:
j = i%2
B[i][j] = A[i][j] * x[j]

I can do the same multiplying if I multiply a 3×3 matrix to a 2×3 vector but I don’t know how to do it.
Do you know how to do it using Numpy and Eigen?


Here is a method using sparse matrices that will allow you to do a rank K QR factorization of your matrices and then multiply the 2 Kx2 matrices so that they both have the same order.
Matrices with the same order are easier to multiply.
Here is some code to help you get started. It uses Eigen’s library and you should be able to easily replace it with an equivalent from scipy or NumPy.
matrix1_size = 2

matrix2_size = 2

matrix1 = np.random.randn(matrix1_size, matrix1_size)

matrix2 = np.random.randn(matrix2_size, matrix2_size)

r_rank1 = [[0]*matrix1_size for i in range

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