def regularization_term(self):
        """ Compute the regularization term of an inversion. This term represents the sum of the difference in flux \
        between every pair of neighboring pixels. This is computed as:

        s_T * H * s = solution_vector.T * regularization_matrix * solution_vector

        The term is referred to as *G_l* in Warren & Dye 2003, Nightingale & Dye 2015.

        The above works include the regularization_matrix coefficient (lambda) in this calculation. In PyAutoLens, \
        this is already in the regularization matrix and thus implicitly included in the matrix multiplication.
        """
        return np.matmul(self.solution_vector.T, np.matmul(self.regularization_matrix, self.solution_vector))
def regularization_term(self):
        """ Compute the regularization term of an inversion. This term represents the sum of the difference in flux \
        between every pair of neighboring pixels. This is computed as:

        s_T * H * s = solution_vector.T * regularization_matrix * solution_vector

        The term is referred to as *G_l* in Warren & Dye 2003, Nightingale & Dye 2015.

        The above works include the regularization_matrix coefficient (lambda) in this calculation. In PyAutoLens, \
        this is already in the regularization matrix and thus implicitly included in the matrix multiplication.
        """
        return np.matmul(self.solution_vector.T, np.matmul(self.regularization_matrix, self.solution_vector))
def regularization_term ( self ) : return np . matmul ( self . solution_vector . T , np . matmul ( self . regularization_matrix , self . solution_vector ) )
Compute the regularization term of an inversion. This term represents the sum of the difference in flux \
        between every pair of neighboring pixels. This is computed as:

        s_T * H * s = solution_vector.T * regularization_matrix * solution_vector

        The term is referred to as *G_l* in Warren & Dye 2003, Nightingale & Dye 2015.

        The above works include the regularization_matrix coefficient (lambda) in this calculation. In PyAutoLens, \
        this is already in the regularization matrix and thus implicitly included in the matrix multiplication.
Compute the regularization term of an inversion . This term represents the sum of the difference in flux \ between every pair of neighboring pixels . This is computed as :