(self, iszerofunc=_iszero)
return self.adjugate() / d
Calculates the inverse using the adjugate matrix and a determinant.

        See Also
        ========

        inv
        inverse_LU
        inverse_GE
Calculates the inverse using the adjugate matrix and a determinant.
Calculates the inverse using the adjugate matrix and a determinant .
def inverse_ADJ(self, iszerofunc=_iszero):
        """Calculates the inverse using the adjugate matrix and a determinant.

        See Also
        ========

        inv
        inverse_LU
        inverse_GE
        """
        if not self.is_square:
            raise NonSquareMatrixError("A Matrix must be square to invert.")

        d = self.berkowitz_det()
        zero = d.equals(0)
        if zero is None:
            # if equals() can't decide, will rref be able to?
            ok = self.rref(simplify=True)[0]
            zero = any(iszerofunc(ok[j, j]) for j in range(ok.rows))
        if zero:
            raise ValueError("Matrix det == 0; not invertible.")

        return self.adjugate() / d
def inverse_ADJ ( self , iszerofunc = _iszero ) : if not self . is_square : raise NonSquareMatrixError ( "A Matrix must be square to invert." ) d = self . berkowitz_det ( ) zero = d . equals ( 0 ) if zero is None : # if equals() can't decide, will rref be able to? ok = self . rref ( simplify = True ) [ 0 ] zero = any ( iszerofunc ( ok [ j , j ] ) for j in range ( ok . rows ) ) if zero : raise ValueError ( "Matrix det == 0; not invertible." ) return self . adjugate ( ) / d