mzszym / sparsegrad Goto Github PK

Hi,
my objective function is:

def F(x):
    A = np.diag(np.random.rand(100))
    return np.diag(x**2) @ A @ x

For x0 = np.random.rand(100), the command:
F(ad.seed(x0))
returns:
ValueError: Input must be 1- or 2-d.

Is a matrix multiplication in an objective function unsupported?

This library does not support matrix operations
For example this does not work

import numpy as np
import sparsegrad.forward as ad
def f(x):
    return x-x[::-1,::-1]
x=np.zeros((3,3))
print(f(ad.seed(x)).dvalue)

Would it be possible to add support for operations on matrices and Nd arrays ?
For the reference here is a matlab implementation for forward differentiation that support nd arrays.