carpet/loss_gradient.py
def synthesis_primal_grad(z, A, L, x):
    """ Gradient for the primal formulation of the synthesis problem. """
    z = np.atleast_2d(z)
    LA = L.dot(A)
    grad = (z.dot(LA) - x).dot(LA.T)
    return grad
 Gradient for the primal formulation of the synthesis problem. 
Gradient for the primal formulation of the synthesis problem.
Gradient for the primal formulation of the synthesis problem .
def synthesis_primal_grad(z, A, L, x):
    z = np.atleast_2d(z)
    LA = L.dot(A)
    grad = (z.dot(LA) - x).dot(LA.T)
    return grad
def synthesis_primal_grad ( z , A , L , x ) : z = np . atleast_2d ( z ) LA = L . dot ( A ) grad = ( z . dot ( LA ) - x ) . dot ( LA . T ) return grad
Gradient for the primal formulation of the synthesis problem.
Gradient for the primal formulation of the synthesis problem .
""" Gradient for the primal formulation of the synthesis problem. """
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