Hi Everyone I am python newbie I have to implement lasso L1 regression for a class assignment. This involves solving a quadratic equation involving block matrices.

minimize x^t * H * x + f^t * x

where x > 0

Where H is a 2 X 2 block matrix with each element being a k dimensional matrix and x and f being a 2 X 1 vectors each element being a k dimension vector.

I was thinking of using nd arrays.

such that

  np.shape(H) = (2, 2, k, k)
  np.shape(x) = (2, k)

But I figured out that np.dot(X, H) doesn't work here. Is there an easy way to solve this problem? Thanks in advance.